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 bayes un expected bernstein inequality"):
  Abstract: We present a new PAC-Bayesian generalization bound. Standard bounds contain a $\sqrt{L_n \cdot \operatorname{KL}/n}$ complexity term which dominates unless $L_n$, the empirical error of the learning algorithm's randomized predictions, vanishes. We manage to replace $L_n$ by a term which vanishes in many more situations, essentially whenever the employed learning algorithm is sufficiently stable on the dataset at hand. Our new bound consistently beats state-of-the-art bounds both on a toy example and on UCI datasets (with large enough $n$). Theoretically, unlike existing bounds, our new bound can be expected to converge to $0$ faster whenever a Bernstein/Tsybakov condition holds, thus connecting PAC-Bayesian generalization and excess risk bounds --- for the latter it has long been known that faster convergence can be obtained under Bernstein conditions. Our main technical tool is a new concentration inequality which is like Bernstein's but with $X^2$ taken outside its expectation.

Includes members:
  Member "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "fast rates with high probability in exp concave statistical learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "fast rates for general unbounded loss functions from erm to generalized bayes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consistency and finite sample behavior of binary class probability estimation")
  Member "combining adversarial guarantees and stochastic fast rates in online learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast rates for online prediction with abstention")
  Member "fast rates in statistical and online learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "strength from weakness fast learning using weak supervision")
  Member "from stochastic mixability to fast rates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning with non convex truncated losses by sgd")
  Member "a note on the pac bayesian theorem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probably approximately correct vision based planning using motion primitives")

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)