human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "compressive sensing techniques for next generation wireless communications"
  Abstract: A range of efficient wireless processes and enabling techniques are put under a magnifier glass in the quest for exploring different manifestations of correlated processes, where sub-Nyquist sampling may be invoked as an explicit benefit of having a sparse transform-domain representation. For example, wide-band next-generation systems require a high Nyquist-sampling rate, but the channel impulse response (CIR) will be very sparse at the high Nyquist frequency, given the low number of reflected propagation paths. This motivates the employment of compressive sensing based processing techniques for frugally exploiting both the limited radio resources and the network infrastructure as efficiently as possible. A diverse range of sophisticated compressed sampling techniques is surveyed and we conclude with a variety of promising research ideas related to large-scale antenna arrays, non-orthogonal multiple access (NOMA), and ultra-dense network (UDN) solutions, just to name a few.

Connected through hyperedge HE-1 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo"):
  with members:
    - "adversarial attack on dl based massive mimo csi feedback"
    - "20 years of evolution from cognitive to intelligent communications"
    - "cnn based analog csi feedback in fdd mimo ofdm systems"
    - "deep learning based limited feedback designs for mimo systems"
    - "bit level optimized neural network for multi antenna channel quantization"
    - "an efficient deep learning framework for low rate massive mimo csi reporting"
    - "compression and acceleration of neural networks for communications"
    - "overcoming the channel estimation barrier in massive mimo communication systems"

Connected through hyperedge HE-2 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"):
  with members:
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"

Connected through hyperedge HE-3 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing"):
  with members:
    - "cooperative service caching and workload scheduling in mobile edge computing"
    - "ai assisted low information latency wireless networking"
    - "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"
    - "minimum age of information in the internet of things with non uniform status packet sizes"
    - "content popularity prediction towards location aware mobile edge caching"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "edge caching wireless networks performance analysis and optimization"

Connected through hyperedge HE-4 (induced by source paper "gmd based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter wave massive mimo"):
  with members:
    - "joint active and passive beamforming optimization for intelligent reflecting surface assisted swipt under qos constraints"
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems"
    - "passive beamforming and information transfer via large intelligent surface"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "intelligent reflecting surface enhanced ofdm channel estimation and reflection optimization"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"

Connected through hyperedge HE-5 (induced by source paper "data driven deep learning to design pilot and channel estimator for massive mimo"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "over sampling codebook based hybrid minimum sum mean square error precoding for millimeter wave 3d mimo"
    - "deep learning in physical layer communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning for massive mimo csi feedback"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"

Connected through hyperedge HE-6 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"

Connected through hyperedge HE-7 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "periodic analog channel estimation aided beamforming for massive mimo systems"
    - "device to device communications enabled energy efficient multicast scheduling in mmwave small cells"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "hybrid beamforming with selection for multiuser massive mimo systems"
    - "massive mimo channel estimation for millimeter wave systems via matrix completion"
    - "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"

Connected through hyperedge HE-8 (induced by source paper "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"):
  with members:
    - "wideband hybrid precoding for next generation backhaul fronthaul based on mmwave fd mimo"
    - "over sampling codebook based hybrid minimum sum mean square error precoding for millimeter wave 3d mimo"
    - "stochastic successive convex optimization for two timescale hybrid precoding in massive mimo"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "channel estimation for millimeter wave multiuser mimo systems via parafac decomposition"
    - "hierarchical codebook design for beamforming training in millimeter wave communication"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)