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: "capacity approximations for gaussian relay networks"
  Abstract: Consider a Gaussian relay network where a source node communicates to a destination node with the help of several layers of relays. Recent work has shown that compress-and-forward based strategies can achieve the capacity of this network within an additive gap. Here, the relays quantize their received signals at the noise level and map them to random Gaussian codebooks. The resultant gap to capacity is independent of the SNRs of the channels in the network and the topology but is linear in the total number of nodes. #R##N#In this paper, we provide an improved lower bound on the rate achieved by compress-and-forward based strategies (noisy network coding in particular) in arbitrary Gaussian relay networks, whose gap to capacity depends on the network not only through the total number of nodes but also through the degrees of freedom of the min cut of the network. We illustrate that for many networks, this refined lower bound can lead to a better approximation of the capacity. In particular, we demonstrate that it leads to a logarithmic rather than linear capacity gap in the total number of nodes for certain classes of layered networks. The improvement comes from quantizing the received signals of the relays at a resolution decreasing with the total number of nodes in the network. This suggests that the rule-of-thumb in literature of quantizing the received signals at the noise level 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"
    - "the arbitrarily varying multiple access channel with conferencing encoders"
    - "secure communication over parallel relay channel"
    - "achieving the capacity of the n relay gaussian diamond network within log n bits"

Connected through hyperedge HE-2 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks"):
  with members:
    - "generalized degrees freedom of noncoherent mimo with asymmetric links"
    - "cooperative relaying at finite snr role of quantize map and forward"
    - "wireless network information flow a deterministic approach"
    - "the approximate capacity of the gaussian n relay diamond network"
    - "on noncoherent fading relay channels at high signal to noise ratio"
    - "wireless network simplification the gaussian n relay diamond network"
    - "approximately achieving gaussian relay network capacity with lattice codes"
    - "generalized degrees of freedom of the symmetric gaussian k user interference channel"

Connected through hyperedge HE-3 (induced by source paper "the arbitrarily varying gaussian relay channel with sender frequency division"):
  with members:
    - "the arbitrarily varying relay channel"
    - "a new coding paradigm for the primitive relay channel"
    - "arbitrarily varying wiretap channels with type constrained states"
    - "cut set bound is loose for gaussian relay networks"

Connected through hyperedge HE-4 (induced by source paper "on capacity of non coherent diamond networks"):
  with members:
    - "the approximate capacity of the gaussian n relay diamond network"
    - "on noncoherent fading relay channels at high signal to noise ratio"
    - "wireless network simplification the gaussian n relay diamond network"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-5 (induced by source paper "a novel cooperative strategy for wireless multihop backhaul networks"):
  with members:
    - "an algebraic approach to physical layer network coding"
    - "multihop backhaul compression for the uplink of cloud radio access networks"
    - "two birds and one stone gaussian interference channel with a shared out of band relay of limited rate"
    - "coding and system design for quantize map and forward relaying"
    - "short message noisy network coding with a decode forward option"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "wireless network information flow a deterministic approach"
    - "integer forcing linear receivers"

Connected through hyperedge HE-6 (induced by source paper "capacity and power scaling laws for finite antenna mimo amplify and forward relay networks"):
  with members:
    - "asymptotic performance analysis of a k hop amplify and forward relay mimo channel"
    - "asymptotic capacity and optimal precoding in mimo multi hop relay networks"
    - "ergodic capacity analysis of amplify and forward mimo dual hop systems"
    - "on information rates of the fading wyner cellular model via the thouless formula for the strip"
    - "diversity of mimo multihop relay channels"
    - "crystallization in large wireless networks"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc networks"

Connected through hyperedge HE-7 (induced by source paper "approximate capacity of the mimo relay channel"):
  with members:
    - "distributed decode forward for relay networks"
    - "the optimal input distribution for partial decode and forward in the mimo relay channel"
    - "wireless network information flow a deterministic approach"
    - "transmit signal and bandwidth optimization in multiple antenna relay channels"
    - "slepian wolf coding over cooperative relay networks"
    - "gaussian relay channel capacity to within a fixed number of bits"
    - "rate bounds for mimo relay channels"

Connected through hyperedge HE-8 (induced by source paper "achieving the capacity of the n relay gaussian diamond network within log n bits"):
  with members:
    - "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"
    - "wireless network simplification the gaussian n relay diamond network"
    - "on the capacity of the half duplex diamond channel"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "a theory of network equivalence parts i and ii"

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)