human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "capacity approximations for gaussian relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the arbitrarily varying relay channel")
  Member "a unified relay framework with both d f and c f relay nodes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noncoherent relaying in energy harvesting communication systems")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "the approximate capacity of the gaussian n relay diamond network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalized degrees of freedom of noncoherent diamond networks")
  Member "computation alignment capacity approximation without noise accumulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "expanding the compute and forward framework unequal powers signal levels and multiple linear combinations")
  Member "wireless network simplification the gaussian n relay diamond network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "on the capacity of the half duplex diamond channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "on the optimal compressions in the compress and forward relay schemes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes")

Question: Please tell me which class the source paper 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)