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: "high dimensional csi acquisition in massive mimo sparsity inspired approaches"
  Abstract: Massive MIMO has been regarded as one of the key technologies for 5G wireless networks, as it can significantly improve both the spectral efficiency and energy efficiency. The availability of high-dimensional channel side information (CSI) is critical for its promised performance gains, but the overhead of acquiring CSI may potentially deplete the available radio resources. Fortunately, it has recently been discovered that harnessing various sparsity structures in massive MIMO channels can lead to significant overhead reduction, and thus improve the system performance. This paper presents and discusses the use of sparsity-inspired CSI acquisition techniques for massive MIMO, as well as the underlying mathematical theory. Sparsity-inspired approaches for both frequency-division duplexing and time-division duplexing massive MIMO systems will be examined and compared from an overall system perspective, including the design trade-offs between the two duplexing modes, computational complexity of acquisition algorithms, and applicability of sparsity structures. Meanwhile, some future prospects for research on high-dimensional CSI acquisition to meet practical demands will be identified.

Connected through hyperedge HE-1 (induced by source paper "skin mimo vibration based mimo communication over human skin"):
  with members:
    - "blazeface sub millisecond neural face detection on mobile gpus"
    - "transfer learning for low resource neural machine translation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "distributed base station a concept system for long range broadband wireless access"):
  with members:
    - "a scalable architecture for distributed receive beamforming analysis and experimental demonstration"
    - "antenna grouping based feedback compression for fdd based massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "airsync enabling distributed multiuser mimo with full spatial multiplexing"

Connected through hyperedge HE-3 (induced by source paper "selective uplink training for massive mimo systems"):
  with members:
    - "massive mimo ten myths and one critical question"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "optimal channel training in uplink network mimo systems"
    - "multiuser mimo achievable rates with downlink training and channel state feedback"

Connected through hyperedge HE-4 (induced by source paper "learning based resource allocation scheme for tdd based cran system"):
  with members:
    - "joint time domain resource partitioning rate allocation and video quality adaptation in heterogeneous cellular networks"
    - "learning based adaptive transmission for limited feedback multiuser mimo ofdm"
    - "five disruptive technology directions for 5g"
    - "predicting a user s next cell with supervised learning based on channel states"

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)