human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "learning to branch"
    - "adabatch efficient gradient aggregation rules for sequential and parallel stochastic gradient methods"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning to learn by gradient descent by gradient descent"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "densely connected convolutional networks"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"

Connected through hyperedge HE-2 (induced by source paper "automl using metadata language embeddings"):
  with members:
    - "an open source automl benchmark"
    - "fast task aware architecture inference"
    - "automatic machine learning by pipeline synthesis using model based reinforcement learning and a grammar"
    - "automl a survey of the state of the art"
    - "survey on automated machine learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "automated machine learning via admm"):
  with members:
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "autostacker a compositional evolutionary learning system"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "selecting near optimal learners via incremental data allocation"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-4 (induced by source paper "an admm based framework for automl pipeline configuration"):
  with members:
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "autostacker a compositional evolutionary learning system"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "selecting near optimal learners via incremental data allocation"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-5 (induced by source paper "automatic machine learning by pipeline synthesis using model based reinforcement learning and a grammar"):
  with members:
    - "autostacker a compositional evolutionary learning system"
    - "layered tpot speeding up tree based pipeline optimization"
    - "thinking fast and slow with deep learning and tree search"
    - "openml networked science in machine learning"

Question: Please tell me which class the center node 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)