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 "mixed noisy network coding and cooperative unicasting in wireless networks"):
  Abstract: The problem of communicating a single message to a destination in presence of multiple relay nodes, referred to as cooperative unicast network, is considered. First, we introduce "Mixed Noisy Network Coding" (MNNC) scheme which generalizes "Noisy Network Coding" (NNC) where relays are allowed to decode-and-forward (DF) messages while all of them (without exception) transmit noisy descriptions of their observations. These descriptions are exploited at the destination and the DF relays aim to decode the transmitted messages while creating full cooperation among the nodes. Moreover, the destination and the DF relays can independently select the set of descriptions to be decoded or treated as interference. This concept is further extended to multi-hopping scenarios, referred to as "Layered MNNC" (LMNNC), where DF relays are organized into disjoint groups representing one hop in the network. For cooperative unicast additive white Gaussian noise (AWGN) networks we show that -provided DF relays are properly chosen- MNNC improves over all previously established constant gaps to the cut-set bound. Secondly, we consider the composite cooperative unicast network where the channel parameters are randomly drawn before communication starts and remain fixed during the transmission. Each draw is assumed to be unknown at the source and fully known at the destination but only partly known at the relays. We introduce through MNNC scheme the concept of "Selective Coding Strategy" (SCS) that enables relays to decide dynamically whether, in addition to communicate noisy descriptions, is possible to decode and forward messages. It is demonstrated through slow-fading AWGN relay networks that SCS clearly outperforms conventional coding schemes.

Includes members:
  Member "short message noisy network coding with a decode forward option":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding")
  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 "compute and forward strategies for cooperative distributed antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private information retrieval over gaussian mac")
  Member "selective coding strategy for unicast composite networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a unified relay framework with both d f and c f relay nodes")
  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 "the degrees of freedom of compute and forward":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding")
  Member "short message quantize forward network coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity approximations for gaussian relay networks")
  Member "slepian wolf coding over cooperative relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding")

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)