human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "guaranteed validity for empirical approaches to adaptive data analysis"):
  Abstract: We design a general framework for answering adaptive statistical queries that focuses on providing explicit confidence intervals along with point estimates. Prior work in this area has either focused on providing tight confidence intervals for specific analyses, or providing general worst-case bounds for point estimates. Unfortunately, as we observe, these worst-case bounds are loose in many settings --- often not even beating simple baselines like sample splitting. Our main contribution is to design a framework for providing valid, instance-specific confidence intervals for point estimates that can be generated by heuristics. When paired with good heuristics, this method gives guarantees that are orders of magnitude better than the best worst-case bounds. We provide a Python library implementing our method.

Includes members:
  Member "a new analysis of differential privacy s generalization guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "natural analysts in adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a necessary and sufficient stability notion for adaptive generalization")
  Member "model similarity mitigates test set overuse":
    also connected through other hyperedges:
      (none)
  Member "generalization for adaptively chosen estimators via stable median":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "closure properties for private classification and online prediction")
  Member "information theoretic analysis of generalization capability of learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "calibrating noise to variance in adaptive data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "concentrated differential privacy simplifications extensions and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "max information differential privacy and post selection hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning about generalization via conditional mutual information")

Question: Please tell me which class the source paper 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)