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 "stochastic implicit natural gradient for black box optimization"):
  Abstract: Black-box optimization is primarily important for many compute-intensive applications, including reinforcement learning (RL), robot control, etc. This paper presents a novel theoretical framework for black-box optimization, in which our method performs stochastic update within a trust region defined with KL-divergence. We show that this update is equivalent to a natural gradient step w.r.t. natural parameters of an exponential-family distribution. Theoretically, we prove the convergence rate of our framework for convex functions. Our theoretical results also hold for non-differentiable black-box functions. Empirically, our method achieves superior performance compared with the state-of-the-art method CMA-ES on separable benchmark test problems.

Includes members:
  Member "diagonal acceleration for covariance matrix adaptation evolution strategies":
    also connected through other hyperedges:
      (none)
  Member "provable smoothness guarantees for black box variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provable gradient variance guarantees for black box variational inference")
  Member "efficient batch black box optimization with deterministic regret bounds":
    also connected through other hyperedges:
      (none)
  Member "structured evolution with compact architectures for scalable policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attacking vision based perception in end to end autonomous driving models")
  Member "conjugate computation variational inference converting variational inference in non conjugate models to inferences in conjugate models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "training binary neural networks using the bayesian learning rule")
  Member "relative loss bounds for on line density estimation with the exponential family of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "statistical exponential families a digest with flash cards":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")

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)