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: "arimoto r enyi conditional entropy and bayesian m ary hypothesis testing"
  Abstract: This paper gives upper and lower bounds on the minimum error probability of Bayesian $M$-ary hypothesis testing in terms of the Arimoto-Renyi conditional entropy of an arbitrary order $\alpha$. The improved tightness of these bounds over their specialized versions with the Shannon conditional entropy ($\alpha=1$) is demonstrated. In particular, in the case where $M$ is finite, we show how to generalize Fano's inequality under both the conventional and list-decision settings. As a counterpart to the generalized Fano's inequality, allowing $M$ to be infinite, a lower bound on the Arimoto-Renyi conditional entropy is derived as a function of the minimum error probability. Explicit upper and lower bounds on the minimum error probability are obtained as a function of the Arimoto-Renyi conditional entropy for both positive and negative $\alpha$. Furthermore, we give upper bounds on the minimum error probability as functions of the Renyi divergence. In the setup of discrete memoryless channels, we analyze the exponentially vanishing decay of the Arimoto-Renyi conditional entropy of the transmitted codeword given the channel output when averaged over a random coding ensemble.

Connected through hyperedge HE-1 (induced by source paper "generalizing bottleneck problems"):
  with members:
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"
    - "bounds between contraction coefficients"
    - "deep learning and the information bottleneck principle"
    - "dissipation of information in channels with input constraints"

Connected through hyperedge HE-2 (induced by source paper "information theoretic perspectives on brascamp lieb inequality and its reverse"):
  with members:
    - "strengthening the entropy power inequality"
    - "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "converses for distributed estimation via strong data processing inequalities"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "on non interactive simulation of joint distributions"
    - "a note on the strong data processing inequalities in bayesian networks"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"

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)