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: "information measures the curious case of the binary alphabet"
  Abstract: Four problems related to information divergence measures defined on finite alphabets are considered. In three of the cases we consider, we illustrate a contrast that arises between the binary-alphabet and larger alphabet settings. This is surprising in some instances, since characterizations for the larger alphabet settings do not generalize their binary-alphabet counterparts. In particular, we show that    $f $   -divergences are not the unique decomposable divergences on binary alphabets that satisfy the data processing inequality, thereby clarifying claims that have previously appeared in the literature. We also show that Kullback–Leibler (KL) divergence is the unique Bregman divergence, which is also an    $f $   -divergence for any alphabet size. We show that KL divergence is the unique Bregman divergence, which is invariant to statistically sufficient transformations of the data, even when nondecomposable divergences are considered. Like some of the problems we consider, this result holds only when the alphabet size is at least three.

Connected through hyperedge HE-1 (induced by source paper "on a generalization of the jensen shannon divergence"):
  with members:
    - "on the jensen shannon symmetrization of distances relying on abstract means"
    - "on f divergences integral representations local behavior and inequalities"
    - "monte carlo information geometry the dually flat case"
    - "jeffreys centroids a closed form expression for positive histograms and a guaranteed tight approximation for frequency histograms"
    - "the burbea rao and bhattacharyya centroids"
    - "a family of statistical symmetric divergences based on jensen s inequality"
    - "measures of distributional similarity"

Connected through hyperedge HE-2 (induced by source paper "bounded rational decision making from elementary computations that reduce uncertainty"):
  with members:
    - "systems of bounded rational agents with information theoretic constraints"
    - "bounded rational decision making with adaptive neural network priors"
    - "a short characterization of relative entropy"
    - "regularised deep reinforcement learning with guaranteed convergence"
    - "an information theoretic optimality principle for deep reinforcement learning"
    - "a minimum relative entropy principle for learning and acting"
    - "provably bounded optimal agents"

Connected through hyperedge HE-3 (induced by source paper "bregman divergence bounds and universality properties of the logarithmic loss"):
  with members:
    - "on f divergences integral representations local behavior and inequalities"
    - "on the universality of the logistic loss function"
    - "universality of logarithmic loss in lossy compression"
    - "opening the black box of deep neural networks via information"
    - "f divergence inequalities"
    - "generalized independent component analysis over finite alphabets"
    - "justification of logarithmic loss via the benefit of side information"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-4 (induced by source paper "the bregman chord divergence"):
  with members:
    - "guaranteed bounds on information theoretic measures of univariate mixtures using piecewise log sum exp inequalities"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-5 (induced by source paper "on the universality of the logistic loss function"):
  with members:
    - "f divergence inequalities"

Connected through hyperedge HE-6 (induced by source paper "quantum information on spectral sets"):
  with members:
    - "divergence and sufficiency for convex optimization"

Connected through hyperedge HE-7 (induced by source paper "divergence and sufficiency for convex optimization"):
  with members:
    - "quantum information on spectral sets"

Connected through hyperedge HE-8 (induced by source paper "dependence measures bounding the exploration bias for general measurements"):
  with members:
    - "two measures of dependence"
    - "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)