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: "calibrating noise to variance in adaptive data analysis"
  Abstract: Datasets are often used multiple times and each successive analysis may depend on the outcome of previous analyses. Standard techniques for ensuring generalization and statistical validity do not account for this adaptive dependence. A recent line of work studies the challenges that arise from such adaptive data reuse by considering the problem of answering a sequence of "queries" about the data distribution where each query may depend arbitrarily on answers to previous queries. #R##N#The strongest results obtained for this problem rely on differential privacy -- a strong notion of algorithmic stability with the important property that it "composes" well when data is reused. However the notion is rather strict, as it requires stability under replacement of an arbitrary data element. The simplest algorithm is to add Gaussian (or Laplace) noise to distort the empirical answers. However, analysing this technique using differential privacy yields suboptimal accuracy guarantees when the queries have low variance. Here we propose a relaxed notion of stability that also composes adaptively. We demonstrate that a simple and natural algorithm based on adding noise scaled to the standard deviation of the query provides our notion of stability. This implies an algorithm that can answer statistical queries about the dataset with substantially improved accuracy guarantees for low-variance queries. The only previous approach that provides such accuracy guarantees is based on a more involved differentially private median-of-means algorithm and its analysis exploits stronger "group" stability of the algorithm.

Connected through hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information"):
  with members:
    - "robust generalization via α mutual information"
    - "in defense of uniform convergence generalization via derandomization with an application to interpolating predictors"
    - "strengthened information theoretic bounds on the generalization error"
    - "tightening mutual information based bounds on generalization error"
    - "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate"
    - "a new analysis of differential privacy s generalization guarantees"
    - "pac learning with stable and private predictions"
    - "a direct sum result for the information complexity of learning"

Connected through hyperedge HE-2 (induced by source paper "private learning implies online learning an efficient reduction"):
  with members:
    - "passing tests without memorizing two models for fooling discriminators"
    - "efficient private algorithms for learning halfspaces"
    - "private pac learning implies finite littlestone dimension"
    - "learning in non convex games with an optimization oracle"
    - "how to use heuristics for differential privacy"
    - "oracle efficient online learning and auction design"
    - "differentially private release and learning of threshold functions"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"

Connected through hyperedge HE-3 (induced by source paper "a new analysis of differential privacy s generalization guarantees"):
  with members:
    - "a necessary and sufficient stability notion for adaptive generalization"
    - "the advantages of multiple classes for reducing overfitting from test set reuse"
    - "guaranteed validity for empirical approaches to adaptive data analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "generalization bounds for uniformly stable algorithms"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "subgaussian tail bounds via stability arguments"

Connected through hyperedge HE-4 (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"
    - "learners that use little information"
    - "learners that leak little information"
    - "typicality based stability and privacy"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "adaptive learning with robust generalization guarantees"

Connected through hyperedge HE-5 (induced by source paper "guaranteed validity for empirical approaches to adaptive data analysis"):
  with members:
    - "a new analysis of differential privacy s generalization guarantees"
    - "natural analysts in adaptive data analysis"
    - "model similarity mitigates test set overuse"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "max information differential privacy and post selection hypothesis testing"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-6 (induced by source paper "design of communication systems using deep learning a variational inference perspective"):
  with members:
    - "learning to detect"
    - "constrained cnn losses for weakly supervised segmentation"
    - "one bit ofdm receivers via deep learning"
    - "end to end deep learning of optical fiber communications"
    - "deep learning in physical layer communications"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "model free training of end to end communication systems"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-7 (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"
    - "learners that use little information"
    - "dependence measures bounding the exploration bias for general measurements"
    - "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-8 (induced by source paper "the structure of optimal private tests for simple hypotheses"):
  with members:
    - "improved differentially private analysis of variance"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "test without trust optimal locally private distribution testing"
    - "locally private hypothesis testing"
    - "inspectre privately estimating the unseen"
    - "generalization for adaptively chosen estimators via stable median"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "differentially private identity and closeness testing of discrete distributions"

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)