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 "a new analysis of differential privacy s generalization guarantees"):
  Abstract: We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and gives structural insights that we expect will be useful elsewhere. We show: 1) that differential privacy ensures that the expectation of any query on the posterior distribution on datasets induced by the transcript of the interaction is close to its true value on the data distribution, and 2) sample accuracy on its own ensures that any query answer produced by the mechanism is close to its posterior expectation with high probability. This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the posterior distribution after the mechanism has committed to its answers. The transfer theorem then follows by summing these two bounds, and in particular, avoids the "monitor argument" used to derive high probability bounds in prior work. An upshot of our new proof technique is that the concrete bounds we obtain are substantially better than the best previously known bounds, even though the improvements are in the constants, rather than the asymptotics (which are known to be tight). As we show, our new bounds outperform the naive "sample-splitting" baseline at dramatically smaller dataset sizes compared to the previous state of the art, bringing techniques from this literature closer to practicality.

Includes members:
  Member "a necessary and sufficient stability notion for adaptive generalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an equivalence between private classification and online prediction")
  Member "the advantages of multiple classes for reducing overfitting from test set reuse":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mdea malware detection with evolutionary adversarial learning")
  Member "guaranteed validity for empirical approaches to adaptive data analysis":
    also connected through other hyperedges:
      (none)
  Member "mitigating bias in adaptive data gathering via differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond")
  Member "generalization bounds for uniformly stable algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "generalization for adaptively chosen estimators via stable median":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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-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)