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: "computing bounds on network capacity regions as a polytope reconstruction problem"
  Abstract: We define a notion of network capacity region of networks that generalizes the notion of network capacity defined by Cannons et al. and prove its notable properties such as closedness, boundedness and convexity when the finite field is fixed. We show that the network routing capacity region is a computable rational polytope and provide exact algorithms and approximation heuristics for computing the region. We define the semi-network linear coding capacity region, with respect to a fixed finite field, that inner bounds the corresponding network linear coding capacity region, show that it is a computable rational polytope, and provide exact algorithms and approximation heuristics. We show connections between computing these regions and a polytope reconstruction problem and some combinatorial optimization problems, such as the minimum cost directed Steiner tree problem. We provide an example to illustrate our results. The algorithms are not necessarily polynomial-time.

Connected through hyperedge HE-1 (induced by source paper "a linear network code construction for general integer connections based on the constraint satisfaction problem"):
  with members:
    - "on multi source networks enumeration rate region computation and hierarchy"
    - "the two unicast problem"
    - "precoding based network alignment for three unicast sessions"
    - "achievable rate regions for network coding"
    - "index coding an interference alignment perspective"
    - "scalar linear solvability of matroidal networks associated with representable matroids"
    - "on the index coding problem and its relation to network coding and matroid theory"

Connected through hyperedge HE-2 (induced by source paper "linear fractional network coding and representable discrete polymatroids"):
  with members:
    - "achievable rate regions for network coding"
    - "on the vector linear solvability of networks and discrete polymatroids"
    - "a matroidal framework for network error correcting codes"
    - "scalar linear solvability of matroidal networks associated with representable matroids"
    - "on the index coding problem and its relation to network coding and matroid theory"

Connected through hyperedge HE-3 (induced by source paper "linear network coding linear index coding and representable discrete polymatroids"):
  with members:
    - "achievable rate regions for network coding"
    - "an equivalence between network coding and index coding"
    - "a matroidal framework for network error correcting codes"
    - "scalar linear solvability of matroidal networks associated with representable matroids"
    - "on the index coding problem and its relation to network coding and matroid theory"

Connected through hyperedge HE-4 (induced by source paper "achievable rate regions for network coding"):
  with members:
    - "network coding capacity regions via entropy functions"
    - "non shannon information inequalities in four random variables"
    - "lexicographic products and the power of non linear network coding"
    - "on network coding capacity matroidal networks and network capacity regions"

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)