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: "two measures of dependence"
  Abstract: Two families of dependence measures between random variables are introduced. They are based on the R\'enyi divergence of order $\alpha$ and the relative $\alpha$-entropy, respectively, and both dependence measures reduce to Shannon's mutual information when their order $\alpha$ is one. The first measure shares many properties with the mutual information, including the data-processing inequality, and can be related to the optimal error exponents in composite hypothesis testing. The second measure does not satisfy the data-processing inequality, but appears naturally in the context of distributed task encoding.

Connected through hyperedge HE-1 (induced by source paper "transportation proofs of r enyi entropy power inequalities"):
  with members:
    - "forward and reverse entropy power inequalities in convex geometry"
    - "optimal transportation to the entropy power inequality"
    - "on renyi entropy power inequalities"
    - "yet another proof of the entropy power inequality"
    - "variants of the entropy power inequality"
    - "renyi divergence and kullback leibler divergence"
    - "the concavity of renyi entropy power"
    - "information theoretic proofs of entropy power inequalities"

Connected through hyperedge HE-2 (induced by source paper "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "uniform random number generation from markov chains non asymptotic and asymptotic analyses"
    - "equivocations exponents and second order coding rates under various r enyi information measures"
    - "universal channel coding for exponential family of channels"
    - "universal channel coding for general output alphabet"
    - "converses for secret key agreement and secure computing"
    - "encoding tasks and renyi entropy"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-3 (induced by source paper "testing against independence and a r enyi information measure"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-4 (induced by source paper "coding for positive rate in the source model key agreement problem"):
  with members:
    - "comparison of channels criteria for domination by a symmetric channel"
    - "on the optimality of secret key agreement via omniscience"
    - "converses for secret key agreement and secure computing"
    - "common information and secret key capacity"
    - "secret key generation for a pairwise independent network model"
    - "the infinite message limit of two terminal interactive source coding"

Connected through hyperedge HE-5 (induced by source paper "distributed task encoding"):
  with members:
    - "distributed storage for data security"
    - "encoding tasks and renyi entropy"
    - "guessing under source uncertainty"

Connected through hyperedge HE-6 (induced by source paper "dependence measures bounding the exploration bias for general measurements"):
  with members:
    - "information measures the curious case of the binary alphabet"
    - "tighter variational representations of f divergences via restriction to probability measures"
    - "on a gel fand yaglom peres theorem for f divergences"

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)