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 "scalable constrained bayesian optimization"):
  Abstract: The global optimization of a high-dimensional black-box function under black-box constraints is a pervasive task in machine learning, control, and engineering. These problems are difficult since the feasible set is typically non-convex and hard to find, in addition to the curses of dimensionality and the heterogeneity of the underlying functions. In particular, these characteristics dramatically impact the performance of Bayesian optimization methods, that otherwise have become the de-facto standard for sample-efficient optimization in unconstrained settings. Due to the lack of sample-efficient methods, practitioners usually fall back to evolutionary strategies or heuristics. We propose the scalable constrained Bayesian optimization (SCBO) algorithm that addresses the above challenges by data-independent transformations of the functions and follows the recent theme of local Bayesian optimization. A comprehensive experimental evaluation demonstrates that SCBO achieves excellent results and outperforms the state-of-the-art methods.

Includes members:
  Member "scalable global optimization via local bayesian optimization":
    also connected through other hyperedges:
      (none)
  Member "scaling gaussian process regression with derivatives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sleipnir deterministic and provably accurate feature expansion for gaussian process regression with derivatives")
  Member "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")

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)