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: "bounds between contraction coefficients"
  Abstract: In this paper, we delineate how the contraction coefficient of the strong data processing inequality for KL divergence can be used to learn likelihood models. We then present an alternative formulation to learn likelihood models that forces the input KL divergence of the data processing inequality to vanish, and achieves a contraction coefficient equivalent to the squared maximal correlation. This formulation turns out to admit a linear algebraic solution. To analyze the performance loss in using this simple but suboptimal procedure, we bound these contraction coefficients in the discrete and finite regime, and prove their equivalence in the Gaussian regime.

Connected through hyperedge HE-1 (induced by source paper "on universal features for high dimensional learning and inference"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "probabilistic clustering using maximal matrix norm couplings"
    - "an information theoretic framework for non linear canonical correlation analysis"
    - "deep generalized canonical correlation analysis"
    - "strong data processing inequalities for channels and bayesian networks"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-2 (induced by source paper "on data processing and majorization inequalities for f divergences with applications"):
  with members:
    - "minimum entropy couplings and their applications"
    - "on f divergences integral representations local behavior and inequalities"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "strong data processing inequalities for channels and bayesian networks"
    - "bounds on the entropy of a function of a random variable and their applications"
    - "f divergence inequalities"
    - "e_ gamma resolvability"
    - "strong data processing inequalities for input constrained additive noise channels"

Connected through hyperedge HE-3 (induced by source paper "correlated embeddings of pairs of dependent random variables"):
  with members:
    - "on the sample complexity of hgr maximal correlation functions"
    - "a survey of multi view representation learning"
    - "an efficient approach to informative feature extraction from multimodal data"
    - "deep generalized canonical correlation analysis"
    - "a generalized power iteration method for solving quadratic problem on the stiefel manifold"
    - "deep variational canonical correlation analysis"
    - "principal inertia components and applications"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "comparison of channels criteria for domination by a symmetric channel"):
  with members:
    - "strong data processing inequalities for channels and bayesian networks"
    - "a note on the strong data processing inequalities in bayesian networks"
    - "dissipation of information in channels with input constraints"
    - "universal polar codes for more capable and less noisy channels and sources"
    - "strong data processing inequalities and phi sobolev inequalities for discrete channels"
    - "capacity regions of two new classes of 2 receiver broadcast channels"

Connected through hyperedge HE-5 (induced by source paper "generalizing bottleneck problems"):
  with members:
    - "arimoto r enyi conditional entropy and bayesian m ary hypothesis testing"
    - "principal inertia components and applications"
    - "opening the black box of deep neural networks via information"
    - "deep learning and the information bottleneck principle"
    - "dissipation of information in channels with input constraints"

Connected through hyperedge HE-6 (induced by source paper "on the contractivity of privacy mechanisms"):
  with members:
    - "hypothesis testing under maximal leakage privacy constraints"
    - "dissipation of information in channels with input constraints"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-7 (induced by source paper "probabilistic clustering using maximal matrix norm couplings"):
  with members:
    - "linear bounds between contraction coefficients for f divergences"
    - "linear information coupling problems"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "principal inertia components and applications"):
  with members:
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "the most informative boolean function conjecture holds for high noise"
    - "on the duality of additivity and tensorization"
    - "managing your private and public data bringing down inference attacks against your privacy"
    - "remarks on the most informative function conjecture at fixed mean"
    - "euclidean information theory of networks"
    - "notes on information theoretic privacy"
    - "an improved upper bound for the most informative boolean function conjecture"

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)