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 capacity and optimal scheduling for the half duplex multiple relay channel"
  Abstract: We study the half-duplex multiple-relay channel (HD-MRC) where every node can either transmit or listen but cannot do both at the same time. We obtain a capacity upper bound based on a max-flow min-cut argument and achievable transmission rates based on the decode-forward (DF) coding strategy, for both the discrete memoryless HD-MRC and the phase-fading HD-MRC. We discover that both the upper bound and the achievable rates are functions of the transmit/listen state (a description of which nodes transmit and which receive). More precisely, they are functions of the time fraction of the different states, which we term a schedule. We formulate the optimal scheduling problem to find an optimal schedule that maximizes the DF rate. The optimal scheduling problem turns out to be a maximin optimization, for which we propose an algorithmic solution. We demonstrate our approach on a four-node multiple-relay channel, obtaining closed-form solutions in certain scenarios. Furthermore, we show that for the received signal-to-noise ratio degraded phase-fading HD-MRC, the optimal scheduling problem can be simplified to a max optimization.

Connected through hyperedge HE-1 (induced by source paper "efficiently finding simple schedules in gaussian half duplex relay line networks"):
  with members:
    - "the approximate optimality of simple schedules for half duplex multi relay networks"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "wireless network information flow a deterministic approach"

Connected through hyperedge HE-2 (induced by source paper "on the optimality of simple schedules for networks with multiple half duplex relays"):
  with members:
    - "cut set bound is loose for gaussian relay networks"
    - "on the gaussian half duplex relay channel"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"
    - "capacity of gaussian channels with duty cycle and power constraints"
    - "efficient capacity computation and power optimization for relay networks"
    - "wireless network information flow a deterministic approach"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-3 (induced by source paper "the approximate optimality of simple schedules for half duplex multi relay networks"):
  with members:
    - "on the optimality of simple schedules for networks with multiple half duplex relays"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "efficient capacity computation and power optimization for relay networks"
    - "learning with submodular functions a convex optimization perspective"
    - "on the capacity of the half duplex diamond channel"

Connected through hyperedge HE-4 (induced by source paper "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"):
  with members:
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "study of gaussian relay channels with correlated noises"
    - "interference channels with source cooperation"
    - "interference channels with destination cooperation"
    - "interference mitigation through limited transmitter cooperation"
    - "approximately optimal wireless broadcasting"

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)