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 "linear codes for broadcasting with noisy side information"):
  Abstract: We consider network coding for a noiseless broadcast channel where each receiver demands a subset of messages available at the transmitter and is equipped with noisy side information in the form an erroneous version of the message symbols it demands. We view the message symbols as elements from a finite field and assume that the number of symbol errors in the noisy side information is upper bounded by a known constant. This communication problem, which we refer to as 'broadcasting with noisy side information' (BNSI), has applications in the re-transmission phase of downlink networks. We derive a necessary and sufficient condition for a linear coding scheme to satisfy the demands of all the receivers in a given BNSI network, and show that syndrome decoding can be used at the receivers to decode the demanded messages from the received codeword and the available noisy side information. We represent BNSI problems as bipartite graphs, and using this representation, classify the family of problems where linear coding provides bandwidth savings compared to uncoded transmission. We provide a simple algorithm to determine if a given BNSI network belongs to this family of problems, i.e., to identify if linear coding provides an advantage over uncoded transmission for the given BNSI problem. We provide lower bounds and upper bounds on the optimal codelength and constructions of linear coding schemes based on linear error correcting codes. For any given BNSI problem, we construct an equivalent index coding problem. A linear code is a valid scheme for a BNSI problem if and only if it is valid for the constructed index coding problem.

Includes members:
  Member "uniprior index coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "index coding rank invariant extensions")
  Member "index coding with erroneous side information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linear index coding with multiple senders and extension to a cellular network")
  Member "the single uniprior index coding problem the single sender case and the multi sender extension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure decentralized pliable index coding")
  Member "optimal scalar linear index codes for one sided neighboring side information problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rate memory trade off for multi access coded caching with uncoded placement")
  Member "error correction for index coding with coded side information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal error correcting delivery scheme for an optimal coded caching scheme with small buffers")
  Member "optimal vector linear index codes for some symmetric side information problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "groupcast index coding problem joint extensions")
  Member "blind index coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "code equivalences between network codes with link errors and index codes with side information errors")
  Member "local graph coloring and index coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "embedded index coding")

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)