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: "achieving the capacity of the n relay gaussian diamond network within log n bits"
  Abstract: We consider the N-relay Gaussian diamond network where a source node communicates to a destination node via N parallel relays through a cascade of a Gaussian broadcast (BC) and a multiple access (MAC) channel. Introduced in 2000 by Schein and Gallager, the capacity of this relay network is unknown in general. The best currently available capacity approximation, independent of the coefficients and the SNR's of the constituent channels, is within an additive gap of 1.3 N bits, which follows from the recent capacity approximations for general Gaussian relay networks with arbitrary topology. #R##N#In this paper, we approximate the capacity of this network within 2 log N bits. We show that two strategies can be used to achieve the information-theoretic cutset upper bound on the capacity of the network up to an additive gap of O(log N) bits, independent of the channel configurations and the SNR's. The first of these strategies is simple partial decode-and-forward. Here, the source node uses a superposition codebook to broadcast independent messages to the relays at appropriately chosen rates; each relay decodes its intended message and then forwards it to the destination over the MAC channel. A similar performance can be also achieved with compress-and-forward type strategies (such as quantize-map-and-forward and noisy network coding) that provide the 1.3 N-bit approximation for general Gaussian networks, but only if the relays quantize their observed signals at a resolution inversely proportional to the number of relay nodes N. This suggest that the rule-of-thumb to quantize the received signals at the noise level in the current literature can be highly suboptimal.

Connected through hyperedge HE-1 (induced by source paper "the arbitrarily varying relay channel"):
  with members:
    - "the capacity of the relay channel solution to cover s problem in the gaussian case"
    - "arbitrarily varying wiretap channels with type constrained states"
    - "cut set bound is loose for gaussian relay networks"
    - "the arbitrarily varying wiretap channel secret randomness stability and super activation"
    - "on the continuity of the secrecy capacity of compound and arbitrarily varying wiretap channels"
    - "capacity approximations for gaussian relay networks"
    - "the arbitrarily varying multiple access channel with conferencing encoders"
    - "secure communication over parallel relay channel"

Connected through hyperedge HE-2 (induced by source paper "generalized compression strategy for the downlink cloud radio access network"):
  with members:
    - "hybrid data sharing and compression strategy for downlink cloud radio access network"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "an uplink downlink duality for cloud radio access network"
    - "on achievability for downlink cloud radio access networks with base station cooperation"
    - "cloud radio access network virtualizing wireless access for dense heterogeneous systems"
    - "distributed decode forward for relay networks"
    - "sparse beamforming and user centric clustering for downlink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "on the capacity of the half duplex mimo gaussian diamond channel"):
  with members:
    - "approximate capacity of the mimo relay channel"
    - "gaussian half duplex relay networks improved constant gap and connections with the assignment problem"
    - "gaussian relay channel capacity to within a fixed number of bits"

Connected through hyperedge HE-5 (induced by source paper "distributed decode forward for relay networks"):
  with members:
    - "partial decode forward relaying for the gaussian two hop relay network"
    - "noisy network coding with partial df"
    - "optimized backhaul compression for uplink cloud radio access network"
    - "joint precoding and multivariate backhaul compression for the downlink of cloud radio access networks"
    - "index coding capacity how far can one go with only shannon inequalities"
    - "short message noisy network coding with a decode forward option"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "robust and efficient distributed compression for cloud radio access networks"

Connected through hyperedge HE-6 (induced by source paper "cut set bound is loose for gaussian relay networks"):
  with members:
    - "cover s open problem the capacity of the relay channel"
    - "improving on the cut set bound via geometric analysis of typical sets"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "improved capacity approximations for gaussian relay networks"
    - "a unified relay framework with both d f and c f relay nodes"
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "computation alignment capacity approximation without noise accumulation"

Connected through hyperedge HE-7 (induced by source paper "capacity bounds for diamond networks with an orthogonal broadcast channel"):
  with members:
    - "generalization of mrs gerber s lemma"
    - "capacity bounds for a class of diamond networks"
    - "a new entropy power inequality for integer valued random variables"
    - "the gaussian multiple access diamond channel"
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "entropy power inequality for a family of discrete random variables"
    - "dependence balance based outer bounds for gaussian networks with cooperation and feedback"

Connected through hyperedge HE-8 (induced by source paper "partial decode forward relaying for the gaussian two hop relay network"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "slepian wolf coding over cooperative relay networks"
    - "wireless network simplification the gaussian n relay diamond network"
    - "noisy network coding"

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)