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: "improved capacity approximations for gaussian relay networks"
  Abstract: Consider a Gaussian relay network where a number of sources communicate to a destination with the help of several layers of relays. Recent work has shown that a compress-and-forward based strategy at the relays can achieve the capacity of this network within an additive gap. In this strategy, the relays quantize their observations at the noise level and map it to a random Gaussian codebook. The resultant capacity gap is independent of the SNR's of the channels in the network but linear in the total number of nodes. #R##N#In this paper, we show that if the relays quantize their signals at a resolution decreasing with the number of nodes in the network, the additive gap to capacity can be made logarithmic in the number of nodes for a class of layered, time-varying wireless relay networks. This suggests that the rule-of-thumb to quantize the received signals at the noise level used for compress-and-forward in the current literature can be highly suboptimal.

Connected through hyperedge HE-1 (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"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"
    - "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-2 (induced by source paper "on the performance of optimized dense device to device wireless networks"):
  with members:
    - "on the optimality of treating interference as noise"
    - "the approximate sum capacity of the symmetric gaussian k user interference channel"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "capacity scaling of wireless ad hoc networks shannon meets maxwell"
    - "capacity of a class of symmetric simo gaussian interference channels within o 1"
    - "compute and forward harnessing interference through structured codes"
    - "generalized degrees of freedom of the symmetric gaussian k user interference channel"
    - "on information theoretic scaling laws for wireless networks"

Connected through hyperedge HE-3 (induced by source paper "capacity approximations for gaussian relay networks"):
  with members:
    - "compress and forward scheme for relay networks backword decoding and connection to bisubmodular flows"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"
    - "wireless network information flow a deterministic approach"
    - "computation alignment capacity approximation without noise accumulation"
    - "short message quantize forward network coding"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "ergodic interference alignment"
    - "compute and forward harnessing interference through structured codes"

Connected through hyperedge HE-4 (induced by source paper "on information flow and feedback in relay networks"):
  with members:
    - "on the impact of a single edge on the network coding capacity"
    - "efficient capacity computation and power optimization for relay networks"
    - "wireless network information flow a deterministic approach"
    - "wireless network simplification the gaussian n relay diamond network"
    - "approximately optimal wireless broadcasting"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "a theory of network equivalence parts i and ii"
    - "approximately achieving gaussian relay network capacity with lattice codes"

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)