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 "oboe collaborative filtering for automl model selection"):
  Abstract: Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning. Automated machine learning (AutoML) seeks to automate these tasks to enable widespread use of machine learning by non-experts. This paper introduces OBOE, a collaborative filtering method for time-constrained model selection and hyperparameter tuning. OBOE forms a matrix of the cross-validated errors of a large number of supervised learning models (algorithms together with hyperparameters) on a large number of datasets, and fits a low rank model to learn the low-dimensional feature vectors for the models and datasets that best predict the cross-validated errors. To find promising models for a new dataset, OBOE runs a set of fast but informative algorithms on the new dataset and uses their cross-validated errors to infer the feature vector for the new dataset. OBOE can find good models under constraints on the number of models fit or the total time budget. To this end, this paper develops a new heuristic for active learning in time-constrained matrix completion based on optimal experiment design. Our experiments demonstrate that OBOE delivers state-of-the-art performance faster than competing approaches on a test bed of supervised learning problems. Moreover, the success of the bilinear model used by OBOE suggests that AutoML may be simpler than was previously understood.

Includes members:
  Member "cf4cf recommending collaborative filtering algorithms using collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learned per instance algorithm selection in scholarly recommender systems")
  Member "autostacker a compositional evolutionary learning system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automated machine learning state of the art and open challenges")
  Member "why are big data matrices approximately low rank":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on robustness of principal component regression")
  Member "hyperparameter optimization a spectral approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "flash fast bayesian optimization for data analytic pipelines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantifying contribution and propagation of error from computational steps algorithms and hyperparameter choices in image classification pipelines")
  Member "openml networked science in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "algorithm runtime prediction methods evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "procrastinating with confidence near optimal anytime adaptive algorithm configuration")
  Member "information theoretic regret bounds for gaussian process optimization in the bandit setting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")

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)