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: "generalized space and frequency index modulation"
  Abstract: Unlike in conventional modulation where information bits are conveyed only through symbols from modulation alphabets defined in the complex plane [e.g., quadrature amplitude modulation (QAM) and phase shift keying (PSK)], in index modulation (IM), additional information bits are conveyed through indexes of certain transmit entities that get involved in the transmission. Transmit antennas in multiantenna systems and subcarriers in multicarrier systems are examples of such transmit entities that can be used to convey additional information bits through indexing. In this paper, we introduce  generalized space and frequency IM , where the indexes of active transmit antennas and subcarriers convey information bits. We first introduce IM in the spatial domain, which is referred to as generalized spatial IM (GSIM). For GSIM, where bits are indexed only in the spatial domain, we derive the expression for achievable rate and easy-to-compute upper and lower bounds on this rate. We show that the achievable rate in GSIM can be more than that in spatial multiplexing and analytically establish the condition under which this can happen. It is noted that GSIM achieves this higher rate using fewer transmit radio-frequency (RF) chains compared with spatial multiplexing. We also propose a Gibbs-sampling-based detection algorithm for GSIM and show that GSIM can achieve better bit error rate (BER) performance than spatial multiplexing. For generalized space–frequency IM (GSFIM), where bits are encoded through indexing in both active antennas and subcarriers, we derive the achievable rate expression. Numerical results show that GSFIM can achieve higher rates compared with conventional multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM). Moreover, BER results show the potential for GSFIM performing better than MIMO-OFDM.

Connected through hyperedge HE-1 (induced by source paper "uplink and downlink transceiver design for ofdm with index modulation in multi user networks"):
  with members:
    - "multiple input multiple output ofdm with index modulation"
    - "a tight upper bound on bit error rate of joint ofdm and multi carrier index keying"

Connected through hyperedge HE-2 (induced by source paper "single cell and multi cell performance analysis of ofdm index modulation"):
  with members:
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "a tractable approach to coverage and rate in cellular networks"
    - "a fresh look at coding for q ary symmetric channels"

Connected through hyperedge HE-3 (induced by source paper "index modulation for 5g striving to do more with less"):
  with members:
    - "index modulation techniques for 5g wireless networks"
    - "on multiple input multiple output ofdm with index modulation for next generation wireless networks"

Connected through hyperedge HE-4 (induced by source paper "on media based modulation using rf mirrors"):
  with members:
    - "on the capacity and performance of generalized spatial modulation"
    - "media based mimo a new frontier in wireless communications"

Connected through hyperedge HE-5 (induced by source paper "frequency and quadrature amplitude modulation for 5g networks"):
  with members:
    - "coverage and rate analysis for millimeter wave cellular networks"
    - "what will 5g be"

Connected through hyperedge HE-6 (induced by source paper "space time index modulation"):
  with members:
    - "generalized spatial modulation in large scale multiuser mimo systems"

Connected through hyperedge HE-7 (induced by source paper "index modulation techniques for 5g wireless networks"):
  with members:
    - "multiple input multiple output ofdm with index modulation"

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)