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 "near optimal quantization and linear network coding for relay networks"):
  Abstract: We introduce a discrete network corresponding to any Gaussian wireless network that is obtained by simply quantizing the received signals and restricting the transmitted signals to a finite precision. Since signals in the discrete network are obtained from those of a Gaussian network, the Gaussian network can be operated on the quantization-based digital interface defined by the discrete network. We prove that this digital interface is near-optimal for Gaussian relay networks and the capacities of the Gaussian and the discrete networks are within a bounded gap of O(M^2) bits, where M is the number of nodes. #R##N#We prove that any near-optimal coding strategy for the discrete network can be naturally transformed into a near-optimal coding strategy for the Gaussian network merely by quantization. We exploit this by designing a linear coding strategy for the case of layered discrete relay networks. The linear coding strategy is near-optimal for Gaussian and discrete networks and achieves rates within O(M^2) bits of the capacity, independent of channel gains or SNR. The linear code is robust and the relays need not know the channel gains. The transmit and receive signals at all relays are simply quantized to binary tuples of the same length $n$ . The linear network code requires all the relay nodes to collect the received binary tuples into a long binary vector and apply a linear transformation on the long vector. The resulting binary vector is split into smaller binary tuples for transmission by the relays. The quantization requirements of the linear network code are completely defined by the parameter $n$, which also determines the resolution of the analog-to-digital and digital-to-analog convertors for operating the network within a bounded gap of the network's capacity. The linear network code explicitly connects network coding for wireline networks with codes for Gaussian networks.

Includes members:
  Member "an algebraic approach to physical layer network coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lattice coding for downlink multiuser transmission")
  Member "expansion coding achieving the capacity of an aen channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polar coding for fading channels")
  Member "multicommodity flows and cuts in polymatroidal networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online energy harvesting problem over an arbitrary directed acyclic graph network")
  Member "capacity of multiple unicast in wireless networks a polymatroidal approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable capacity bounding models for wireless networks")
  Member "wireless network information flow a deterministic approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "best relay selection in gaussian half duplex diamond networks")
  Member "low complexity near optimal codes for gaussian relay networks":
    also connected through other hyperedges:
      (none)
  Member "binary error correcting network codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "end to end error correcting codes on networks with worst case symbol errors")
  Member "a digital interface for gaussian relay and interference networks lifting codes from the discrete superposition model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the optimality of treating interference as noise general message sets")

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)