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: "more general queries and less generalization error in adaptive data analysis"
  Abstract: Adaptivity is an important feature of data analysis---typically the choice of questions asked about a dataset depends on previous interactions with the same dataset. However, generalization error is typically bounded in a non-adaptive model, where all questions are specified before the dataset is drawn. Recent work by Dwork et al. (STOC '15) and Hardt and Ullman (FOCS '14) initiated the formal study of this problem, and gave the first upper and lower bounds on the achievable generalization error for adaptive data analysis. #R##N#Specifically, suppose there is an unknown distribution $\mathcal{P}$ and a set of $n$ independent samples $x$ is drawn from $\mathcal{P}$. We seek an algorithm that, given $x$ as input, "accurately" answers a sequence of adaptively chosen "queries" about the unknown distribution $\mathcal{P}$. How many samples $n$ must we draw from the distribution, as a function of the type of queries, the number of queries, and the desired level of accuracy? #R##N#In this work we make two new contributions towards resolving this question: #R##N#*We give upper bounds on the number of samples $n$ that are needed to answer statistical queries that improve over the bounds of Dwork et al. #R##N#*We prove the first upper bounds on the number of samples required to answer more general families of queries. These include arbitrary low-sensitivity queries and the important class of convex risk minimization queries. #R##N#As in Dwork et al., our algorithms are based on a connection between differential privacy and generalization error, but we feel that our analysis is simpler and more modular, which may be useful for studying these questions in the future.

Connected through hyperedge HE-1 (induced by source paper "algorithmic stability for adaptive data analysis"):
  with members:
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "generalization in adaptive data analysis and holdout reuse"
    - "on the generalization properties of differential privacy"
    - "between pure and approximate differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"
    - "private multiplicative weights beyond linear queries"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

Connected through hyperedge HE-2 (induced by source paper "generalization in adaptive data analysis and holdout reuse"):
  with members:
    - "the ladder a reliable leaderboard for machine learning competitions"
    - "on the generalization properties of differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "characterizing the sample complexity of private learners"
    - "preserving statistical validity in adaptive data analysis"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "calibrating data to sensitivity in private data analysis"
    - "interactive privacy via the median mechanism"

Connected through hyperedge HE-3 (induced by source paper "the ladder a reliable leaderboard for machine learning competitions"):
  with members:
    - "on the generalization properties of differential privacy"
    - "preventing false discovery in interactive data analysis is hard"
    - "preserving statistical validity in adaptive data analysis"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"

Connected through hyperedge HE-4 (induced by source paper "on the generalization properties of differential privacy"):
  with members:
    - "preserving statistical validity in adaptive data analysis"
    - "calibrating data to sensitivity in private data analysis"
    - "what can we learn privately"

Connected through hyperedge HE-5 (induced by source paper "private multiplicative weights beyond linear queries"):
  with members:
    - "preventing false discovery in interactive data analysis is hard"
    - "private learning and sanitization pure vs approximate differential privacy"
    - "preserving statistical validity in adaptive data analysis"
    - "faster private release of marginals on small databases"
    - "efficient algorithms for privately releasing marginals via convex relaxations"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "the power of linear reconstruction attacks"

Connected through hyperedge HE-6 (induced by source paper "preserving statistical validity in adaptive data analysis"):
  with members:
    - "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"
    - "interactive fingerprinting codes and the hardness of preventing false discovery"
    - "calibrating data to sensitivity in private data analysis"
    - "interactive privacy via the median mechanism"

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)