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: "challenges in bayesian adaptive data analysis"
  Abstract: Traditional statistical analysis requires that the analysis process and data are independent. By contrast, the new field of adaptive data analysis hopes to understand and provide algorithms and accuracy guarantees for research as it is commonly performed in practice, as an iterative process of interacting repeatedly with the same data set, such as repeated tests against a holdout set. Previous work has defined a model with a rather strong lower bound on sample complexity in terms of the number of queries, $n\sim\sqrt q$, arguing that adaptive data analysis is much harder than static data analysis, where $n\sim\log q$ is possible. Instead, we argue that those strong lower bounds point to a limitation of the previous model in that it must consider wildly asymmetric scenarios which do not hold in typical applications. #R##N#To better understand other difficulties of adaptivity, we propose a new Bayesian version of the problem that mandates symmetry. Since the other lower bound techniques are ruled out, we can more effectively see difficulties that might otherwise be overshadowed. As a first contribution to this model, we produce a new problem using error-correcting codes on which a large family of methods, including all previously proposed algorithms, require roughly $n\sim\sqrt[4]q$. These early results illustrate new difficulties in adaptive data analysis regarding slightly correlated queries on problems with concentrated uncertainty.

Connected through hyperedge HE-1 (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-2 (induced by source paper "information privacy and stability in adaptive data analysis"):
  with members:
    - "typicality based stability and privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "adaptive learning with robust generalization guarantees"
    - "bayesian adaptive data analysis guarantees from subgaussianity"
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "algorithmic stability for adaptive data analysis"
    - "preventing false discovery in interactive data analysis is hard"

Connected through hyperedge HE-3 (induced by source paper "bayesian adaptive data analysis guarantees from subgaussianity"):
  with members:
    - "the ladder a reliable leaderboard for machine learning competitions"

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)