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 "automated machine learning via admm"):
  Abstract: We study the automated machine learning (AutoML) problem of jointly selecting appropriate algorithms from an algorithm portfolio as well as optimizing their hyper-parameters for certain learning tasks. The main challenges include a) the coupling between algorithm selection and hyper-parameter optimization (HPO), and b) the black-box optimization nature of the problem where the optimizer cannot access the gradients of the loss function but may query function values. To circumvent these difficulties, we propose a new AutoML framework by leveraging the alternating direction method of multipliers (ADMM) scheme. Due to the splitting properties of ADMM, algorithm selection and HPO can be decomposed through the augmented Lagrangian function. As a result, HPO with mixed continuous and integer constraints are efficiently handled through a query-efficient Bayesian optimization approach and Euclidean projection operator that yields a closed-form solution. Algorithm selection in ADMM is naturally interpreted as a combinatorial bandit problem. The effectiveness of our proposed methodology is compared to state-of-the-art AutoML schemes such as TPOT and Auto-sklearn on numerous benchmark data sets.

Includes members:
  Member "oboe collaborative filtering for automl model selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing")
  Member "automated machine learning with monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "meta learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting meta learning as supervised learning")
  Member "zeroth order stochastic variance reduction for nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards an efficient and general framework of robust training for graph neural networks")
  Member "autostacker a compositional evolutionary learning system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automated machine learning state of the art and open challenges")
  Member "hyperband a novel bandit based approach to hyperparameter optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "selecting near optimal learners via incremental data allocation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a system for massively parallel hyperparameter tuning")
  Member "auto weka combined selection and hyperparameter optimization of classification algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unicorn runtime provenance based detector for advanced persistent threats")

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)