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: "mutual information bounds via adjacency events"
  Abstract: The mutual information between two jointly distributed random variables    $X$    and    $Y$    is a functional of the joint distribution    $P_{XY}$   , which is sometimes difficult to handle or estimate. A coarser description of the statistical behavior of    $(X,Y)$    is given by the marginal distributions    $P_{X}, P_{Y}$    and the  adjacency  relation induced by the joint distribution, where    $x$    and    $y$    are adjacent if    $P(x,y)>0$   . We derive a lower bound on the mutual information in terms of these entities. The bound is obtained by viewing the channel from    $X$    to    $Y$    as a probability distribution on a set of possible  actions , where an action determines the output for any possible input, and is independently drawn. We also provide an alternative proof based on convex optimization that yields a generally tighter bound. Finally, we derive an upper bound on the mutual information in terms of adjacency events between the action and the pair    $(X,Y)$   , where in this case, an action    $a$    and a pair    $(x,y)$    are adjacent if    $y=a(x)$   . As an example, we apply our bounds to the binary deletion channel and show that for the special case of an independent identically distributed input distribution and a range of deletion probabilities, our lower and upper bounds both outperform the best known bounds for the mutual information.

Connected through hyperedge HE-1 (induced by source paper "minimum entropy couplings and their applications"):
  with members:
    - "strong functional representation lemma and applications to coding theorems"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "entropic causal inference"
    - "on the entropy of couplings"
    - "sumset and inverse sumset inequalities for differential entropy and mutual information"
    - "entropic inequalities and marginal problems"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "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)