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 gaussian half duplex relay channel"
  Abstract: This paper considers the Gaussian half-duplex relay channel (G-HD-RC): a channel model where a source transmits a message to a destination with the help of a relay that cannot transmit and receive at the same time. It is shown that the cut-set upper bound on the capacity can be achieved to within a constant gap, regardless of the actual value of the channel parameters, by either partial-decode-and-forward or compress-and-forward. The performance of these coding strategies is evaluated with both random and deterministic switch at the relay. Numerical evaluations show that the actual gap is less than what analytically obtained, and that random switch achieves higher rates than deterministic switch. As a result of this analysis, the generalized degrees-of-freedom of the G-HD-RC is exactly characterized for this channel. In order to get insights into practical schemes for the G-HD-RC that are less complex than partial-decode-and-forward or compress-and-forward, the exact capacity of the linear deterministic approximation (LDA) of the G-HD-RC at high signal-to-noise-ratio is determined. It is shown that random switch and correlated nonuniform inputs bits are optimal for the LDA. It is then demonstrated that deterministic switch is to within one bit from the capacity. This latter scheme is translated into a coding strategy for the original G-HD-RC and its optimality to within a constant gap is proved. The gap attained by this scheme is larger than that of partial-decode-and-forward, thereby pointing to an interesting practical tradeoff between gap to capacity and complexity.

Connected through hyperedge HE-1 (induced by source paper "optimal power allocation strategies in full duplex relay networks"):
  with members:
    - "joint source relay design for full duplex mimo af relay systems"
    - "on the capacity of the two hop half duplex relay channel"
    - "capacity of the gaussian two hop full duplex relay channel with residual self interference"
    - "on the capacity of the noncausal relay channel"

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"
    - "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"
    - "on capacity and optimal scheduling for the half duplex multiple relay channel"
    - "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 "on the capacity of the two hop half duplex relay channel"):
  with members:
    - "capacity of the two hop half duplex relay channel"

Connected through hyperedge HE-4 (induced by source paper "capacity of the two hop half duplex relay channel"):
  with members:
    - "bits through deterministic relay cascades with half duplex constraint"
    - "capacity of gaussian channels with duty cycle and power constraints"
    - "wireless network information flow a deterministic approach"

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)