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: "max information differential privacy and post selection hypothesis testing"
  Abstract: In this paper, we initiate a principled study of how the generalization properties of approximate differential privacy can be used to perform adaptive hypothesis testing, while giving statistically valid $p$-value corrections. We do this by observing that the guarantees of algorithms with bounded approximate max-information are sufficient to correct the $p$-values of adaptively chosen hypotheses, and then by proving that algorithms that satisfy $(\epsilon,\delta)$-differential privacy have bounded approximate max information when their inputs are drawn from a product distribution. This substantially extends the known connection between differential privacy and max-information, which previously was only known to hold for (pure) $(\epsilon,0)$-differential privacy. It also extends our understanding of max-information as a partially unifying measure controlling the generalization properties of adaptive data analyses. We also show a lower bound, proving that (despite the strong composition properties of max-information), when data is drawn from a product distribution, $(\epsilon,\delta)$-differentially private algorithms can come first in a composition with other algorithms satisfying max-information bounds, but not necessarily second if the composition is required to itself satisfy a nontrivial max-information bound. This, in particular, implies that the connection between $(\epsilon,\delta)$-differential privacy and max-information holds only for inputs drawn from product distributions, unlike the connection between $(\epsilon,0)$-differential privacy and max-information.

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 identity testing for high dimensional distributions"):
  with members:
    - "lower bounds for testing graphical models colorings and antiferromagnetic ising models"
    - "efficiently estimating erdos renyi graphs with node differential privacy"
    - "improved differentially private analysis of variance"
    - "individual sensitivity preprocessing for data privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "learning and testing causal models with interventions"
    - "privately learning high dimensional distributions"
    - "test without trust optimal locally private distribution testing"

Connected through hyperedge HE-3 (induced by source paper "information theoretic metrics for local differential privacy protocols"):
  with members:
    - "consistent and accurate frequency oracles under local differential privacy"
    - "towards instance optimal private query release"
    - "distributed and secure ml with self tallying multi party aggregation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "renyi differential privacy"
    - "achieving secure and differentially private computations in multiparty settings"
    - "collecting telemetry data privately"
    - "differential privacy as a mutual information constraint"

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"
    - "calibrating noise to variance in adaptive data analysis"
    - "learners that use little information"
    - "learners that leak little information"
    - "typicality based stability and privacy"
    - "inferential privacy guarantees for differentially private mechanisms"

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"
    - "calibrating noise to variance in adaptive data analysis"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-6 (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"
    - "dependence measures bounding the exploration bias for general measurements"
    - "generalization in adaptive data analysis and holdout reuse"

Connected through hyperedge HE-7 (induced by source paper "constructing privacy channels from information channels"):
  with members:
    - "on the information privacy model the group and composition privacy"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "generalization in adaptive data analysis and holdout reuse"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "calibrating data to sensitivity in private 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)