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 "controlled random search improves sample mining and hyper parameter optimization"):
  Abstract: A common challenge in machine learning and related fields is the need to efficiently explore high dimensional parameter spaces using small numbers of samples. Typical examples are hyper-parameter optimization in deep learning and sample mining in predictive modeling tasks. All such problems trade-off exploration, which samples the space without knowledge of the target function, and exploitation where information from previous evaluations is used in an adaptive feedback loop. Much of the recent focus has been on the exploitation while exploration is done with simple designs such as Latin hypercube or even uniform random sampling. In this paper, we introduce optimal space-filling sample designs for effective exploration of high dimensional spaces. Specifically, we propose a new parameterized family of sample designs called space-filling spectral designs, and introduce a framework to choose optimal designs for a given sample size and dimension. Furthermore, we present an efficient algorithm to synthesize a given spectral design. Finally, we evaluate the performance of spectral designs in both data space and model space applications. The data space exploration is targeted at recovering complex regression functions in high dimensional spaces. The model space exploration focuses on selecting hyper-parameters for a given neural network architecture. Our empirical studies demonstrate that the proposed approach consistently outperforms state-of-the-art techniques, particularly with smaller design sizes.

Includes members:
  Member "interpretable and pedagogical examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unexplainability and incomprehensibility of artificial intelligence")
  Member "sampling matters in deep embedding learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "critical hyper parameters no random no cry":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "importance sampling for minibatches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic trust region inexact newton method for large scale machine learning")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a bayesian sampling approach to exploration in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "worst case regret bounds for exploration via randomized value functions")

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)