human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "conditional information inequalities for entropic and almost entropic points":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "integrity constraints revisited from exact to approximate implication")
  Member "on the non robustness of essentially conditional information inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entropy region and convolution")
  Member "entropy functions and determinant inequalities":
    also connected through other hyperedges:
      (none)
  Member "on essentially conditional information inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conditional information inequalities and combinatorial applications")
  Member "non shannon information inequalities in four random variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the storage cost of private information retrieval")
  Member "average entropy functions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analytical framework of ldgm based iterative quantization with decimation")
  Member "information rates of minimal non matroid related access structures":
    also connected through other hyperedges:
      (none)
  Member "a projection method for derivation of non shannon type information inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constrained linear representability of polymatroids and algorithms for computing achievability proofs in network coding")

Question: Please tell me which class the source paper 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)