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: "asymptotic orthogonality analysis of time domain sparse massive mimo channels"
  Abstract: The theoretical analysis of downlink massive MIMO usually assumes the ideal Gaussian channel matrix with asymptotic orthogonality of channel vectors. Meanwhile, recent experiments have shown that massive MIMO channels between a certain user and massive base station antennas appear the spatial common sparsity (SCS) in both the time domain and angle domain. This motivates us to investigate whether realistic sparse massive MIMO channels could provide the favorable propagation condition, and reveal the capacity gap between massive MIMO systems over realistic sparse channels and that under the ideal Gaussian channel matrix assumption. This letter theoretically proves that channel vectors associated with different users in massive MIMO over sparse channels satisfy the asymptotic orthogonality. Moreover, the simulation results confirm the theoretical analysis.

Connected through hyperedge HE-1 (induced by source paper "massive mimo asymptotics for ray based propagation channels"):
  with members:
    - "mimo channel hardening a physical model based analysis"
    - "channel hardening in massive mimo a measurement based analysis"
    - "energy efficiency and asymptotic performance evaluation of beamforming structures in doubly massive mimo mmwave systems"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"
    - "uplink analysis of large mu mimo systems with space constrained arrays in ricean fading"
    - "no downlink pilots are needed in tdd massive mimo"
    - "massive mimo ten myths and one critical question"
    - "massive mimo performance evaluation based on measured propagation data"

Connected through hyperedge HE-2 (induced by source paper "energy efficient downlink transmission for multicell massive das with pilot contamination"):
  with members:
    - "joint power allocation and user association optimization for massive mimo systems"
    - "interference management in 5g reverse tdd hetnets with wireless backhaul a large system analysis"
    - "large scale mimo relaying techniques for physical layer security af or df"
    - "large system analysis of cognitive radio network via partially projected regularized zero forcing precoding"
    - "joint power and antenna selection optimization in large cloud radio access networks"
    - "impact of transceiver power consumption on the energy efficiency of zero forcing detector in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-3 (induced by source paper "beamforming and scheduling for mmwave downlink sparse virtual channels with non orthogonal and orthogonal multiple access"):
  with members:
    - "a survey of downlink non orthogonal multiple access for 5g wireless communication networks"
    - "a multi service oriented multiple access scheme for m2m support in future lte"
    - "a multi service oriented multiple access scheme for next generation mobile networks"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-4 (induced by source paper "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"):
  with members:
    - "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "priori information aided iterative hard threshold a low complexity high accuracy compressive sensing based channel estimation for tds ofdm"
    - "structured compressive sensing based superimposed pilot design in downlink large scale mimo systems"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "super resolution sparse mimo ofdm channel estimation based on spatial and temporal correlations"
    - "massive mimo for next generation wireless systems"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"

Connected through hyperedge HE-5 (induced by source paper "channel estimation for mmwave massive mimo based access and backhaul in ultra dense network"):
  with members:
    - "achievable rate of rician large scale mimo channels with transceiver hardware impairments"
    - "joint csit acquisition based on low rank matrix completion for fdd massive mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "mmwave massive mimo based wireless backhaul for the 5g ultra dense network"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"
    - "structured compressed sensing from theory to applications"

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)