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: "learners that leak little information"
  Abstract: We study learning algorithms that are restricted to using a small amount of information from their input sample. We introduce a category of learning algorithms we term d-bit information learners, which are algorithms whose output conveys at most d bits of information on their input. A central theme in this work is that such algorithms generalize. #R##N#We focus on the learning capacity of these algorithms, and prove sample complexity bounds with tight dependencies on the confidence and error parameters. We also observe connections with well studied notions such as sample compression schemes, Occam's razor, PAC-Bayes and differential privacy. #R##N#We discuss an approach that allows us to prove upper bounds on the amount of information that algorithms reveal about their inputs, and also provide a lower bound by showing a simple concept class for which every (possibly randomized) empirical risk minimizer must reveal a lot of information. On the other hand, we show that in the distribution-dependent setting every VC class has empirical risk minimizers that do not reveal a lot of information.

Connected through hyperedge HE-1 (induced by source paper "guess first to enable better compression and adversarial robustness"):
  with members:
    - "extracting robust and accurate features via a robust information bottleneck"
    - "complement objective training"
    - "sparse dnns with improved adversarial robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "excessive invariance causes adversarial vulnerability"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-2 (induced by source paper "a necessary and sufficient stability notion for adaptive generalization"):
  with members:
    - "natural analysts in adaptive data analysis"
    - "an operational approach to information leakage"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "calibrating noise to variance in adaptive data analysis"
    - "learners that use little information"
    - "typicality based stability and privacy"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "adaptive learning with robust generalization guarantees"

Connected through hyperedge HE-3 (induced by source paper "variational predictive information bottleneck"):
  with members:
    - "on variational bounds of mutual information"
    - "neural processes"
    - "the conditional entropy bottleneck"
    - "learners that use little information"
    - "deep variational information bottleneck"

Connected through hyperedge HE-4 (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"
    - "dependence measures bounding the exploration bias for general measurements"
    - "understanding deep learning requires rethinking generalization"

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.LG(Machine Learning)