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 are optimal for index coding instances with five or fewer receivers"):
  Abstract: We study zero-error unicast index-coding instances, where each receiver must perfectly decode its requested message set, and the message sets requested by any two receivers do not overlap. We show that for all these instances with up to five receivers, linear index codes are optimal. Although this class contains 9847 non-isomorphic instances, by using our recent results and by properly categorizing the instances based on their graphical representations, we need to consider only 13 non-trivial instances to solve the entire class. This work complements the result by Arbabjolfaei et al. (ISIT 2013), who derived the capacity region of all unicast index-coding problems with up to five receivers in the diminishing-error setup. They employed random-coding arguments, which require infinitely-long messages. We consider the zero-error setup; our approach uses graph theory and combinatorics, and does not require long messages.

Includes members:
  Member "a new class of index coding instances where linear coding is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal finite length and asymptotic index codes for five or fewer receivers")
  Member "on the capacity region for index coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an index coding approach to caching with uncoded cache placement")
  Member "duality codes and the integrality gap bound for index coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cooperative multi sender 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)