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 "groupcast index coding problem joint extensions"):
  Abstract: The groupcast index coding problem is the most general version of the classical index coding problem, where any receiver can demand messages that are also demanded by other receivers. Any groupcast index coding problem is described by its \emph{fitting matrix} which contains unknown entries along with $1$'s and $0$'s. The problem of finding an optimal scalar linear code is equivalent to completing this matrix with known entries such that the rank of the resulting matrix is minimized. Any row basis of such a completion gives an optimal \emph{scalar linear} code. An index coding problem is said to be a joint extension of a finite number of index coding problems, if the fitting matrices of these problems are disjoint submatrices of the fitting matrix of the jointly extended problem. In this paper, a class of joint extensions of any finite number of groupcast index coding problems is identified, where the relation between the fitting matrices of the sub-problems present in the fitting matrix of the jointly extended problem is defined by a base problem. A lower bound on the \emph{minrank} (optimal scalar linear codelength) of the jointly extended problem is given in terms of those of the sub-problems. This lower bound also has a dependence on the base problem and is operationally useful in finding lower bounds of the jointly extended problems when the minranks of all the sub-problems are known. We provide an algorithm to construct scalar linear codes (not optimal in general), for any groupcast problem belonging to the class of jointly extended problems identified in this paper. The algorithm uses scalar linear codes of all the sub-problems and the base problem. We also identify some subclasses, where the constructed codes are scalar linear optimal.

Includes members:
  Member "linear codes for broadcasting with noisy side information":
    also connected through other hyperedges:
      (none)
  Member "optimal scalar linear codes for some classes of the two sender groupcast index coding problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the optimal broadcast rate of the two sender unicast index coding problem with fully participated interactions")
  Member "low complexity decoding for symmetric neighboring and consecutive side information index coding problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reduced complexity index codes and improved upperbound on broadcast rate for neighboring interference problems")
  Member "index coding rank invariant extensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal weakly secure linear codes for some classes of the two sender index coding problem")
  Member "structural properties of index coding capacity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on index coding for complementary graphs with focus on circular perfect graphs")
  Member "the single uniprior index coding problem the single sender case and the multi sender extension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure decentralized pliable index coding")
  Member "generalized lexicographic products and the index coding capacity":
    also connected through other hyperedges:
      (none)
  Member "optimal vector linear index codes for some symmetric side information problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "linear codes for broadcasting with noisy side information")

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)