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: "mapping the region of entropic vectors with support enumeration information geometry"
  Abstract: The region of entropic vectors is a convex cone that has been shown to be at the core of many fundamental limits for problems in multiterminal data compression, network coding, and multimedia transmission. This cone has been shown to be non-polyhedral for four or more random variables, however its boundary remains unknown for four or more discrete random variables. Methods for specifying probability distributions that are in faces and on the boundary of the convex cone are derived, then utilized to map optimized inner bounds to the unknown part of the entropy region. The first method utilizes tools and algorithms from abstract algebra to efficiently determine those supports for the joint probability mass functions for four or more random variables that can, for some appropriate set of non-zero probabilities, yield entropic vectors in the gap between the best known inner and outer bounds. These supports are utilized, together with numerical optimization over non-zero probabilities, to provide inner bounds to the unknown part of the entropy region. Next, information geometry is utilized to parameterize and study the structure of probability distributions on these supports yielding entropic vectors in the faces of entropy and in the unknown part of the entropy region.

Connected through hyperedge HE-1 (induced by source paper "constrained linear representability of polymatroids and algorithms for computing achievability proofs in network coding"):
  with members:
    - "on multi source networks enumeration rate region computation and hierarchy"
    - "characterizing the rate region of the 4 3 3 exact repair regenerating codes"
    - "linear network coding linear index coding and representable discrete polymatroids"
    - "path gain algebraic formulation for the scalar linear network coding problem"
    - "a projection method for derivation of non shannon type information inequalities"

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)