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: "partition symmetrical entropy functions"
  Abstract: Let    $ \mathcal {N}=\{1,\ldots ,n\}$   . The entropy function h of a set of    $n$    discrete random variables    $\{X_{i}:i\in \mathcal {N} \}$    is a    $2^{n}$   -dimensional vector whose entries are    $ \text {h}( \mathcal {A})\triangleq H(X_{ \mathcal {A}})$   ,    $ \mathcal {A}\subset \mathcal {N} $   , the (joint) entropies of the subsets of the set of    $n$    random variables with    $H(X_\emptyset )=0$    by convention. The set of all entropy functions for    $n$    discrete random variables, denoted by    $\Gamma ^{*}_{n}$   , is called the entropy region for    $n$   . Characterization of    $\Gamma ^{*}_{n}$    and its closure    $\overline {\Gamma ^{*}_{n}}$    are well-known open problems in information theory. They are important not only because they play key roles in information theory problems but also they are related to other subjects in mathematics and physics. In this paper, we consider  partition-symmetrical entropy functions . Let    $p=\{ \mathcal {N}_{1},\ldots , \mathcal {N}_{t}\}$    be a    $t$   -partition of    $ \mathcal {N}$   . An entropy function    $\bf h$    is called    $p$   -symmetrical if for all    ${\mathcal{ A}},{\mathcal{ B}} \subset {\mathcal{ N}}$   ,    $ \text {h}({\mathcal{ A}}) = \text {h}({\mathcal{ B}})$    whenever    $|{\mathcal{ A}} \cap {\mathcal{ N}}_{i}| = |{\mathcal{ B}} \cap {\mathcal{ N}}_{i}|$   ,    $i = 1, \ldots ,t$   . The set of all the    $p$   -symmetrical entropy functions, denoted by    $\Psi ^{*}_{p}$   , is called    $p$   -symmetrical entropy function region. We prove that    $\overline {\Psi ^{*}_{p}}$   , the closure of    $\Psi ^{*}_{p}$   , is completely characterized by Shannon-type information inequalities if and only if    $p$    is the 1-partition or a 2-partition with one of its blocks being a singleton. The characterization of the partition-symmetrical entropy functions can be useful for solving some information theory and related problems where symmetry exists in the structure of the problems.

Connected through hyperedge HE-1 (induced by source paper "how to use undiscovered information inequalities direct applications of the copy lemma"):
  with members:
    - "entropy region and convolution"
    - "equivalence of two proof techniques for non shannon type inequalities"
    - "non shannon information inequalities in four random variables"
    - "improved upper bounds for the information rates of the secret sharing schemes induced by the vamos matroid"

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)