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: "some properties of renyi entropy over countably infinite alphabets"
  Abstract: We study certain properties of Renyi entropy functionals $$H_\alpha \left( \mathcal{P} \right)$$ on the space of probability distributions over ?+. Primarily, continuity and convergence issues are addressed. Some properties are shown to be parallel to those known in the finite alphabet case, while others illustrate a quite different behavior of the Renyi entropy in the infinite case. In particular, it is shown that for any distribution $$\mathcal{P}$$ and any r ? [0,?] there exists a sequence of distributions $$\mathcal{P}_n$$ converging to $$\mathcal{P}$$ with respect to the total variation distance and such that $$\mathop {\lim }\limits_{n \to \infty } \mathop {\lim }\limits_{\alpha \to 1 + } H_\alpha \left( {\mathcal{P}_n } \right) = \mathop {\lim }\limits_{\alpha \to 1 + } \mathop {\lim }\limits_{n \to \infty } H_\alpha \left( {\mathcal{P}_n } \right) + r$$ .

Connected through hyperedge HE-1 (induced by source paper "generalized fano type inequality for countably infinite systems with list decoding"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "asymptotic coupling and its applications in information theory"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "concentration of measure inequalities in information theory communications and coding second edition"
    - "renyi divergence and kullback leibler divergence"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"

Connected through hyperedge HE-2 (induced by source paper "on the entropy of couplings"):
  with members:
    - "entropy bounds for discrete random variables via maximal coupling"
    - "on the hardness of entropy minimization and related problems"
    - "minimum entropy combinatorial optimization problems"
    - "finding the maximizers of the information divergence from an exponential family"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

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)