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: "preventing false discovery in interactive data analysis is hard"
  Abstract: We show that, under a standard hardness assumption, there is no computationally efficient algorithm that given $n$ samples from an unknown distribution can give valid answers to $n^{3+o(1)}$ adaptively chosen statistical queries. A statistical query asks for the expectation of a predicate over the underlying distribution, and an answer to a statistical query is valid if it is "close" to the correct expectation over the distribution. #R##N#Our result stands in stark contrast to the well known fact that exponentially many statistical queries can be answered validly and efficiently if the queries are chosen non-adaptively (no query may depend on the answers to previous queries). Moreover, a recent work by Dwork et al. shows how to accurately answer exponentially many adaptively chosen statistical queries via a computationally inefficient algorithm; and how to answer a quadratic number of adaptive queries via a computationally efficient algorithm. The latter result implies that our result is tight up to a linear factor in $n.$ #R##N#Conceptually, our result demonstrates that achieving statistical validity alone can be a source of computational intractability in adaptive settings. For example, in the modern large collaborative research environment, data analysts typically choose a particular approach based on previous findings. False discovery occurs if a research finding is supported by the data but not by the underlying distribution. While the study of preventing false discovery in Statistics is decades old, to the best of our knowledge our result is the first to demonstrate a computational barrier. In particular, our result suggests that the perceived difficulty of preventing false discovery in today's collaborative research environment may be inherent.

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"
    - "calibrating noise to variance in adaptive data analysis"
    - "oracle efficient online learning and auction design"
    - "differentially private release and learning of threshold functions"

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"
    - "calibrating noise to variance in adaptive data analysis"

Connected through hyperedge HE-4 (induced by source paper "aggregating votes with local differential privacy usefulness soundness vs indistinguishability"):
  with members:
    - "collecting and analyzing multidimensional data with local differential privacy"
    - "tight lower bounds for locally differentially private selection"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "tight lower bounds for differentially private selection"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "collecting and analyzing data from smart device users with local differential privacy"

Connected through hyperedge HE-5 (induced by source paper "locally private learning without interaction requires separation"):
  with members:
    - "exponential separations in local differential privacy"
    - "the role of interactivity in local differential privacy"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "collecting telemetry data privately"
    - "distributed mean estimation with limited communication"
    - "statistical query algorithms for mean vector estimation and stochastic convex optimization"
    - "the limitations of optimization from samples"

Connected through hyperedge HE-6 (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"
    - "max information differential privacy and post selection hypothesis testing"

Connected through hyperedge HE-7 (induced by source paper "the advantages of multiple classes for reducing overfitting from test set reuse"):
  with members:
    - "do imagenet classifiers generalize to imagenet"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "imagenet large scale visual recognition challenge"
    - "preserving statistical validity in adaptive data analysis"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

Connected through hyperedge HE-8 (induced by source paper "quantitative overfitting management for human in the loop ml application development with ease ml meter"):
  with members:
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "eth ds3lab at semeval 2018 task 7 effectively combining recurrent and convolutional neural networks for relation classification and extraction"
    - "deep contextualized word representations"
    - "patient risk assessment and warning symptom detection using deep attention based neural networks"
    - "ease ml towards multi tenant resource sharing for machine learning workloads"
    - "snorkel rapid training data creation with weak supervision"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "mllib machine learning in apache spark"

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)