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: "dependence measures bounding the exploration bias for general measurements"
  Abstract: We propose a framework to analyze and quantify the bias in adaptive data analysis. It generalizes that proposed by Russo and Zou'15, applying to all measurements whose moment generating function exists, and to all measurements with a finite $p$-norm. We introduce a new class of dependence measures which retain key properties of mutual information while more effectively quantifying the exploration bias for heavy tailed distributions. We provide examples of cases where our bounds are nearly tight in situations where the original framework of Russo and Zou'15 does not apply.

Connected through hyperedge HE-1 (induced by source paper "novel change of measure inequalities and pac bayesian bounds"):
  with members:
    - "scalable information inequalities for uncertainty quantification"
    - "tighter variational representations of f divergences via restriction to probability measures"
    - "pac bayesian inequalities for martingales"

Connected through hyperedge HE-2 (induced by source paper "chaining meets chain rule multilevel entropic regularization and training of neural nets"):
  with members:
    - "distribution dependent analysis of gibbs erm principle"
    - "tightening mutual information based bounds on generalization error"
    - "data dependent pac bayes priors via differential privacy"
    - "generalization error bounds for noisy iterative algorithms"
    - "chaining mutual information and tightening generalization bounds"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"

Connected through hyperedge HE-3 (induced by source paper "generalization error bounds via renyi f divergences and maximal leakage"):
  with members:
    - "tightening mutual information based bounds on generalization error"
    - "generalization error bounds for noisy iterative algorithms"
    - "an operational approach to information leakage"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "learners that use little information"
    - "max information differential privacy and post selection hypothesis testing"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-4 (induced by source paper "strengthened information theoretic bounds on the generalization error"):
  with members:
    - "information privacy and stability in adaptive data analysis"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "learners that use little information"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"

Connected through hyperedge HE-5 (induced by source paper "tightening mutual information based bounds on generalization error"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "chaining mutual information and tightening generalization bounds"
    - "information theoretic analysis of generalization capability of learning algorithms"

Connected through hyperedge HE-6 (induced by source paper "chaining mutual information and tightening generalization bounds"):
  with members:
    - "generalization error bounds for noisy iterative algorithms"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "learners that leak little information"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-7 (induced by source paper "two measures of dependence"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "distributed task encoding"
    - "f divergence inequalities"
    - "minimization problems based on relative alpha entropy ii reverse projection"
    - "encoding tasks and renyi entropy"
    - "minimization problems based on relative alpha entropy i forward projection"
    - "renyi divergence and kullback leibler divergence"
    - "guessing under source uncertainty"

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)