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: "on the universality of the logistic loss function"
  Abstract: A loss function measures the discrepancy between the true values (observations) and their estimated fits, for a given instance of data. A loss function is said to be proper (unbiased, Fisher consistent) if the fits are defined over a unit simplex, and the minimizer of the expected loss is the true underlying probability of the data. Typical examples are the zero-one loss, the quadratic loss and the Bernoulli log-likelihood loss (log-loss). In this work we show that for binary classification problems, the divergence associated with smooth, proper and convex loss functions is bounded from above by the Kullback-Leibler (KL) divergence, up to a multiplicative normalization constant. It implies that by minimizing the log-loss (associated with the KL divergence), we minimize an upper bound to any choice of loss functions from this set. This property justifies the broad use of log-loss in regression, decision trees, deep neural networks and many other applications. In addition, we show that the KL divergence bounds from above any separable Bregman divergence that is convex in its second argument (up to a multiplicative normalization constant). This result introduces a new set of divergence inequalities, similar to the well-known Pinsker inequality.

Connected through hyperedge HE-1 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "machine learning using the variational predictive information bottleneck with a validation set"
    - "on the difference between the information bottleneck and the deep information bottleneck"
    - "extracting robust and accurate features via a robust information bottleneck"
    - "general information bottleneck objectives and their applications to machine learning"
    - "vector gaussian ceo problem under logarithmic loss"
    - "on universal features for high dimensional learning and inference"
    - "estimation efficiency under privacy constraints"

Connected through hyperedge HE-2 (induced by source paper "vector gaussian ceo problem under logarithmic loss and applications"):
  with members:
    - "vector gaussian ceo problem under logarithmic loss"
    - "chaining mutual information and tightening generalization bounds"
    - "minimax learning for remote prediction"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "distributed information bottleneck method for discrete and gaussian sources"
    - "universality of logarithmic loss in lossy compression"
    - "a generalization of blahut arimoto algorithm to compute rate distortion regions of multiterminal source coding under logarithmic loss"
    - "principal inertia components and applications"

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"
    - "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"
    - "information measures the curious case of the binary alphabet"

Connected through hyperedge HE-4 (induced by source paper "innovation representation of stochastic processes with application to causal inference"):
  with members:
    - "lossless and lossy compression of random forests"
    - "bregman divergence bounds and the universality of the logarithmic loss"
    - "entropic causal inference"
    - "large alphabet source coding using independent component analysis"
    - "learning causal graphs with small interventions"
    - "generalized independent component analysis over finite alphabets"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "on the hardness of entropy minimization and related problems"

Connected through hyperedge HE-5 (induced by source paper "non linear canonical correlation analysis a compressed representation approach"):
  with members:
    - "bregman divergence bounds and universality properties of the logarithmic loss"
    - "stochastic approximation for canonical correlation analysis"
    - "on deep multi view representation learning objectives and optimization"
    - "nonparametric canonical correlation analysis"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-6 (induced by source paper "lossless and lossy compression of random forests"):
  with members:
    - "bregman divergence bounds and universality properties of the logarithmic loss"
    - "deep forest"
    - "deep forest towards an alternative to deep neural networks"
    - "generalized independent component analysis over finite alphabets"

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)