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 maximal correlation hypercontractivity and the data processing inequality studied by erkip and cover"
  Abstract: In this paper we provide a new geometric characterization of the Hirschfeld-Gebelein-R\'{e}nyi maximal correlation of a pair of random $(X,Y)$, as well as of the chordal slope of the nontrivial boundary of the hypercontractivity ribbon of $(X,Y)$ at infinity. The new characterizations lead to simple proofs for some of the known facts about these quantities. We also provide a counterexample to a data processing inequality claimed by Erkip and Cover, and find the correct tight constant for this kind of inequality.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "phase transitions for the information bottleneck in representation learning"):
  with members:
    - "dynamics aware unsupervised discovery of skills"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "the conditional entropy bottleneck"
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "autoaugment learning augmentation policies from data"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-3 (induced by source paper "communication for generating correlation a unifying survey"):
  with members:
    - "secret key capacity for multipleaccess channel with public feedback"
    - "exact exponent for soft covering"
    - "exact channel synthesis"
    - "strong converse using change of measure arguments"
    - "coding for positive rate in the source model key agreement problem"
    - "wiretap channels nonasymptotic fundamental limits"
    - "channel resolvability theorems for general sources and channels"
    - "strong functional representation lemma and applications to coding theorems"

Connected through hyperedge HE-4 (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-5 (induced by source paper "repairing without retraining avoiding disparate impact with counterfactual distributions"):
  with members:
    - "algorithmic fairness"
    - "from soft classifiers to hard decisions how fair can we be"
    - "on the direction of discrimination an information theoretic analysis of disparate impact in machine learning"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "fairness testing testing software for discrimination"
    - "algorithmic decision making and the cost of fairness"
    - "proxy non discrimination in data driven systems"

Connected through hyperedge HE-6 (induced by source paper "learnability for the information bottleneck"):
  with members:
    - "the conditional entropy bottleneck"
    - "the dynamics of differential learning i information dynamics and task reachability"
    - "autoaugment learning augmentation policies from data"
    - "strong data processing inequalities for channels and bayesian networks"
    - "emergence of invariance and disentanglement in deep representations"
    - "deep variational information bottleneck"
    - "wide residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "maximal correlation and the rate of fisher information convergence in the central limit theorem"):
  with members:
    - "monotonicity of entropy and fisher information a quick proof via maximal correlation"
    - "generalized entropy power inequalities and monotonicity properties of information"

Connected through hyperedge HE-8 (induced by source paper "the conditional entropy bottleneck"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "learnability for the information bottleneck"
    - "scaling provable adversarial defenses"
    - "uncertainty in the variational information bottleneck"
    - "implicit reparameterization gradients"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "autoaugment learning augmentation policies from data"
    - "representation learning with contrastive predictive coding"

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)