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: "non stationarities in extra large scale massive mimo"
  Abstract: Massive MIMO, a key technology for increasing area spectral efficiency in cellular systems, was developed assuming moderately sized apertures. In this paper, we argue that massive MIMO systems behave differently in large-scale regimes due to spatial non-stationarity. In the large-scale regime, with arrays of around fifty wavelengths, the terminals see the whole array but non-stationarities occur because different regions of the array see different propagation paths. At even larger dimensions, which we call the extra-large scale regime, terminals see a portion of the array and inside the first type of non-stationarities might occur. We show that the non-stationarity properties of the massive MIMO channel changes several important MIMO design aspects. In simulations, we demonstrate how non-stationarity is a curse when neglected but a blessing when embraced in terms of computational load and multi-user transceiver design.

Connected through hyperedge HE-1 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction"):
  with members:
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "multi band covariance interpolation with applications in massive mimo"
    - "yolov3 an incremental improvement"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "channel estimation for tdd fdd massive mimo systems with channel covariance computing"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-2 (induced by source paper "multiple antenna technologies for beyond 5g"):
  with members:
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "robust trajectory and resource allocation design for secure uav aided communications"
    - "a new look at cell free massive mimo making it practical with dynamic cooperation"
    - "scalability aspects of cell free massive mimo"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "robust and secure wireless communications via intelligent reflecting surfaces"
    - "channel estimation for intelligent reflecting surface assisted multiuser communications framework algorithms and analysis"
    - "resource allocation for secure irs assisted multiuser miso systems"

Connected through hyperedge HE-3 (induced by source paper "massive mimo for internet of things iot connectivity"):
  with members:
    - "massive mimo unsourced random access"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "ubiquitous cell free massive mimo communications"
    - "sparcs for unsourced random access"
    - "management of network slicing in 5g radio access networks functional framework and information models"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "massive mimo for ultra reliable communications with constellations for dual coherent noncoherent detection"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"

Connected through hyperedge HE-4 (induced by source paper "computation offloading for iot in c ran optimization and deep learning"):
  with members:
    - "cross layer design for mission critical iot in mobile edge computing systems"
    - "two timescale hybrid compression and forward for massive mimo aided c ran"
    - "learning to optimize training deep neural networks for interference management"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "deep learning in mobile and wireless networking a survey"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"

Connected through hyperedge HE-5 (induced by source paper "enabling large intelligent surfaces with compressive sensing and deep learning"):
  with members:
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "generative adversarial estimation of channel covariance in vehicular millimeter wave systems"
    - "machine learning for reliable mmwave systems blockage prediction and proactive handoff"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "an overview of signal processing techniques for millimeter wave mimo systems"

Connected through hyperedge HE-6 (induced by source paper "compression and acceleration of neural networks for communications"):
  with members:
    - "learning to detect"
    - "deep learning in physical layer communications"
    - "deep learning based csi feedback approach for time varying massive mimo channels"
    - "an introduction to deep learning for the physical layer"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep learning for massive mimo csi feedback"

Connected through hyperedge HE-7 (induced by source paper "on the uplink transmission of multi user extra large scale massive mimo systems"):
  with members:
    - "capacity analysis for spatially non wide sense stationary uplink massive mimo systems"
    - "towards very large aperture massive mimo a measurement based study"
    - "massive mimo performance evaluation based on measured propagation data"
    - "on the matrix inversion approximation based on neumann series in massive mimo systems"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-8 (induced by source paper "channel estimation for extremely large scale massive mimo systems"):
  with members:
    - "massive mimo channel estimation taking into account spherical waves"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"

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)