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 "pac learning with stable and private predictions"):
  Abstract: We study binary classification algorithms for which the prediction on any point is not too sensitive to individual examples in the dataset. Specifically, we consider the notions of uniform stability (Bousquet and Elisseeff, 2001) and prediction privacy (Dwork and Feldman, 2018). Previous work on these notions shows how they can be achieved in the standard PAC model via simple aggregation of models trained on disjoint subsets of data. Unfortunately, this approach leads to a significant overhead in terms of sample complexity. Here we demonstrate several general approaches to stable and private prediction that either eliminate or significantly reduce the overhead. Specifically, we demonstrate that for any class $C$ of VC dimension $d$ there exists a $\gamma$-uniformly stable algorithm for learning $C$ with excess error $\alpha$ using $\tilde O(d/(\alpha\gamma) + d/\alpha^2)$ samples. We also show that this bound is nearly tight. For $\epsilon$-differentially private prediction we give two new algorithms: one using $\tilde O(d/(\alpha^2\epsilon))$ samples and another one using $\tilde O(d^2/(\alpha\epsilon) + d/\alpha^2)$ samples. The best previously known bounds for these problems are $O(d/(\alpha^2\gamma))$ and $O(d/(\alpha^3\epsilon))$, respectively.

Includes members:
  Member "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning about generalization via conditional mutual information")
  Member "privately answering classification queries in the agnostic pac model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an equivalence between private classification and online prediction")
  Member "towards demystifying membership inference attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data and model dependencies of membership inference attack")
  Member "model agnostic private learning via stability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "how to use heuristics for differential privacy")
  Member "private pac learning implies finite littlestone dimension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an equivalence between private classification and online prediction")
  Member "privacy preserving prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privgen preserving privacy of sequences through data generation")
  Member "towards measuring membership privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "subgaussian tail bounds via stability arguments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a new analysis of differential privacy s generalization guarantees")

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)