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: "on the fundamental limits of mimo massive multiple access channels"
  Abstract: In this paper, we study multiple-antenna wireless communication networks where a large number of devices simultaneously communicating with an access point. The capacity region of multiple-input multiple-output massive multiple access channels (MIMO mMAC) is investigated. While the joint typicality decoding is utilized to establish the achievability of capacity region for conventional multiple access channel with fixed number of users, the technique is not directly applicable for MIMO mMAC [4]. Instead, an information-theoretic approach based on Gallager's error exponent analysis is exploited to characterize the capacity region of MIMO mMAC. Theoretical results reveal that the capacity region of MIMO mMAC is dominated by the sum rate constraint only and the individual user rate is determined by a specific factor that corresponds to the allocation of sum rate. The individual user rate in conventional MAC is not achievable with massive multiple access and the successive interference cancellation cannot guarantee an arbitrary small decoding error probability for MIMO mMAC. The capacity region of MIMO mMAC is a generalization of the symmetric rate in [5].

Connected through hyperedge HE-1 (induced by source paper "fundamental limits of many user mac with finite payloads and fading"):
  with members:
    - "compressed neighbour discovery using sparse kerdock matrices"
    - "capacity of gaussian many access channels"
    - "quasi static simo fading channels at finite blocklength"
    - "quasi static multiple antenna fading channels at finite blocklength"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"

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)