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 "on locally decodable index codes"):
  Abstract: Index coding achieves bandwidth savings by jointly encoding the messages demanded by all the clients in a broadcast channel. The encoding is performed in such a way that each client can retrieve its demanded message from its side information and the broadcast codeword. In general, in order to decode its demanded message symbol, a receiver may have to observe the entire transmitted codeword. Querying or downloading the codeword symbols might involve costs to a client -- such as network utilization costs and storage requirements for the queried symbols to perform decoding. In traditional index coding solutions, this 'client aware' perspective is not considered during code design. As a result, for these codes, the number of codeword symbols queried by a client per decoded message symbol, which we refer to as 'locality', could be large. In this paper, considering locality as a cost parameter, we view index coding as a trade-off between the achievable broadcast rate (codeword length normalized by the message length) and locality, where the objective is to minimize the broadcast rate for a given value of locality and vice versa. We show that the smallest possible locality for any index coding problem is 1, and that the optimal index coding solution with locality 1 is the coding scheme based on fractional coloring of the interference graph. We propose index coding schemes with small locality by covering the side information graph using acyclic subgraphs and subgraphs with small minrank. We also show how locality can be accounted for in conventional partition multicast and cycle covering solutions to index coding. Finally, applying these new techniques, we characterize the locality-broadcast rate trade-off of the index coding problem whose side information graph is the directed 3-cycle.

Includes members:
  Member "reduced complexity index codes and improved upperbound on broadcast rate for neighboring interference problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "index codes with minimum locality for three receiver unicast problems")
  Member "privacy in index coding k limited access schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "index codes with minimum locality for three receiver unicast problems")
  Member "speeding up distributed machine learning using codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gcsa codes with noise alignment for secure coded multi party batch matrix multiplication")
  Member "preserving privacy while broadcasting k limited access schemes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy in index coding improved bounds and coding schemes")
  Member "local partial clique covers for index coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the optimality of partial clique covering for index coding")
  Member "on a duality between recoverable distributed storage and index coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "embedded index coding")
  Member "graph theory versus minimum rank for index coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "embedded index coding")
  Member "topological interference management through index coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the separability of parallel miso broadcast channels under partial csit a degrees of freedom region perspective")

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)