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: "average entropy functions"
  Abstract: The closure of the set of entropy functions associated with n discrete variables, Gammar*n, is a convex cone in (2n-1)- dimensional space, but its full characterization remains an open problem. In this paper, we map Gammar*n to an n-dimensional region Phi*n by averaging the joint entropies with the same number of variables, and show that the simpler Phi*n can be characterized solely by the Shannon-type information inequalities

Connected through hyperedge HE-1 (induced by source paper "partition symmetrical entropy functions"):
  with members:
    - "conditional information inequalities for entropic and almost entropic points"
    - "on the non robustness of essentially conditional information inequalities"
    - "entropy functions and determinant inequalities"
    - "on essentially conditional information inequalities"
    - "non shannon information inequalities in four random variables"
    - "information rates of minimal non matroid related access structures"
    - "a projection method for derivation of non shannon type information inequalities"
    - "dualities between entropy functions and network codes"

Connected through hyperedge HE-2 (induced by source paper "analytical framework of ldgm based iterative quantization with decimation"):
  with members:
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "design and analysis of ldgm based codes for mse quantization"
    - "the case for structured random codes in network capacity theorems"
    - "low density graph codes that are optimal for source channel coding and binning"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "analysis of ldgm and compound codes for lossy compression and binning"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"

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)