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 "generalization bounds for uniformly stable algorithms"):
  Abstract: Uniform stability of a learning algorithm is a classical notion of algorithmic stability introduced to derive high-probability bounds on the generalization error (Bousquet and Elisseeff, 2002). Specifically, for a loss function with range bounded in $[0,1]$, the generalization error of a $\gamma$-uniformly stable learning algorithm on $n$ samples is known to be within $O((\gamma +1/n) \sqrt{n \log(1/\delta)})$ of the empirical error with probability at least $1-\delta$. Unfortunately, this bound does not lead to meaningful generalization bounds in many common settings where $\gamma \geq 1/\sqrt{n}$. At the same time the bound is known to be tight only when $\gamma = O(1/n)$. #R##N#We substantially improve generalization bounds for uniformly stable algorithms without making any additional assumptions. First, we show that the bound in this setting is $O(\sqrt{(\gamma + 1/n) \log(1/\delta)})$ with probability at least $1-\delta$. In addition, we prove a tight bound of $O(\gamma^2 + 1/n)$ on the second moment of the estimation error. The best previous bound on the second moment is $O(\gamma + 1/n)$. Our proofs are based on new analysis techniques and our results imply substantially stronger generalization guarantees for several well-studied algorithms.

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 "privacy preserving prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privgen preserving privacy of sequences through data generation")
  Member "data dependent stability of stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the generalization of bayesian deep nets for multi class classification")
  Member "subgaussian tail bounds via stability arguments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a new analysis of differential privacy s generalization guarantees")
  Member "concentration bounds for high sensitivity functions through differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "high probability generalization bounds for uniformly stable algorithms with nearly optimal rate")
  Member "a pac bayesian analysis of randomized learning with application to stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the generalization of bayesian deep nets for multi class classification")
  Member "bolt on differential privacy for scalable stochastic gradient descent based analytics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "generalization of erm in stochastic convex optimization the dimension strikes back":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)