human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "low complexity decoding for symmetric neighboring and consecutive side information index coding problems"
  Abstract: The capacity of symmetric, neighboring and consecutive side-information single unicast index coding problems (SNC-SUICP) with number of messages equal to the number of receivers was given by Maleki, Cadambe and Jafar. For these index coding problems, an optimal index code construction by using Vandermonde matrices was proposed. This construction requires all the side-information at the receivers to decode their wanted messages and also requires large field size. In an earlier work, we constructed binary matrices of size $m \times n (m\geq n)$ such that any $n$ adjacent rows of the matrix are linearly independent over every field. Calling these matrices as Adjacent Independent Row (AIR) matrices using which we gave an optimal scalar linear index code for the one-sided SNC-SUICP for any given number of messages and one-sided side-information. By using Vandermonde matrices or AIR matrices, every receiver needs to solve $K-D$ equations with $K-D$ unknowns to obtain its wanted message, where $K$ is the number of messages and $D$ is the size of the side-information. In this paper, we analyze some of the combinatorial properties of the AIR matrices. By using these properties, we present a low-complexity decoding which helps to identify a reduced set of side-information for each users with which the decoding can be carried out. By this method every receiver is able to decode its wanted message symbol by simply adding some index code symbols (broadcast symbols). We explicitly give both the reduced set of side-information and the broadcast messages to be used by each receiver to decode its wanted message. For a given pair or receivers our decoding identifies which one will perform better than the other when the broadcast channel is noisy.

Connected through hyperedge HE-1 (induced by source paper "reduced complexity index codes and improved upperbound on broadcast rate for neighboring interference problems"):
  with members:
    - "near optimal vector linear index codes for single unicast index coding problems with symmetric neighboring interference"
    - "capacity of some index coding problems with symmetric neighboring interference"
    - "optimal scalar linear index codes for one sided neighboring side information problems"
    - "topological interference management through index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"

Connected through hyperedge HE-2 (induced by source paper "a generalisation of interlinked cycle structures and their index coding capacity"):
  with members:
    - "interlinked cycles for index coding generalizing cycles and cliques"
    - "optimal scalar linear index codes for one sided neighboring side information problems"
    - "index coding an interference alignment perspective"

Connected through hyperedge HE-3 (induced by source paper "groupcast index coding problem joint extensions"):
  with members:
    - "linear codes for broadcasting with noisy side information"
    - "optimal scalar linear codes for some classes of the two sender groupcast index coding problem"
    - "index coding rank invariant extensions"
    - "structural properties of index coding capacity"
    - "the single uniprior index coding problem the single sender case and the multi sender extension"
    - "generalized lexicographic products and the index coding capacity"
    - "optimal vector linear index codes for some symmetric side information problems"
    - "a lifting construction for scalar linear index codes"

Connected through hyperedge HE-4 (induced by source paper "on the broadcast rate of index coding problems with symmetric and consecutive interference"):
  with members:
    - "near optimal vector linear index codes for single unicast index coding problems with symmetric neighboring interference"
    - "a new upperbound on the broadcast rate of index coding problems with symmetric neighboring interference"
    - "optimal scalar linear index codes for one sided neighboring side information problems"
    - "topological interference management through index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"

Connected through hyperedge HE-5 (induced by source paper "optimal scalar linear index codes for symmetric and neighboring side information problems"):
  with members:
    - "reduced dimensional optimal vector linear index codes for index coding problems with symmetric neighboring and consecutive side information"
    - "topological interference management through index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"
    - "error correction for index coding with side information"

Connected through hyperedge HE-6 (induced by source paper "reduced dimensional optimal vector linear index codes for index coding problems with symmetric neighboring and consecutive side information"):
  with members:
    - "optimal scalar linear index codes for one sided neighboring side information problems"
    - "topological interference management through index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"

Connected through hyperedge HE-7 (induced by source paper "a new upperbound on the broadcast rate of index coding problems with symmetric neighboring interference"):
  with members:
    - "capacity of some index coding problems with symmetric neighboring interference"
    - "optimal scalar linear index codes for one sided neighboring side information problems"
    - "topological interference management through index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"

Connected through hyperedge HE-8 (induced by source paper "near optimal vector linear index codes for single unicast index coding problems with symmetric neighboring interference"):
  with members:
    - "optimal scalar linear index codes for one sided neighboring side information problems"
    - "topological interference management through index coding"
    - "index coding an interference alignment perspective"
    - "optimal index codes for a class of multicast networks with receiver side information"

Question: Please tell me which class the center node 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)