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: "efficient hyperparameter optimization and infinitely many armed bandits"
  Abstract: Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While current methods offer efficiencies by adaptively choosing new configurations to train, an alternative strategy is to adaptively allocate resources across the selected configurations. We formulate hyperparameter optimization as a pure-exploration non-stochastic infinitely many armed bandit problem where allocation of additional resources to an arm corresponds to training a configuration on larger subsets of the data. We introduce Hyperband for this framework and analyze its theoretical properties, providing several desirable guarantees. We compare Hyperband with state-of-the-art Bayesian optimization methods and a random search baseline on a comprehensive benchmark including 117 datasets. Our results on this benchmark demonstrate that while Bayesian optimization methods do not outperform random search trained for twice as long, Hyperband in favorable settings offers valuable speedups.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-3 (induced by source paper "dynamic task weighting methods for multi task networks in autonomous driving systems"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "which tasks should be learned together in multi task learning"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-4 (induced by source paper "benchmark and survey of automated machine learning frameworks"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning in practice state of the art and recent results"
    - "alphaclean automatic generation of data cleaning pipelines"
    - "automl a survey of the state of the art"
    - "automated machine learning with monte carlo tree search"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"

Connected through hyperedge HE-5 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits"):
  with members:
    - "competing bandits in matching markets"
    - "social learning in multi agent multi armed bandits"
    - "decentralized cooperative stochastic multi armed bandits"
    - "competing bandits learning under competition"
    - "distributed cooperative decision making in multiarmed bandits frequentist and bayesian algorithms"
    - "collaborative learning of stochastic bandits over a social network"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "distributed clustering of linear bandits in peer to peer networks"

Connected through hyperedge HE-6 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "population based augmentation efficient learning of augmentation policy schedules"):
  with members:
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "data augmentation by pairing samples for images classification"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "autoaugment learning augmentation policies from data"
    - "efficient neural architecture search via parameter sharing"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "survey on automated machine learning"):
  with members:
    - "an open source automl benchmark"
    - "automated machine learning state of the art and open challenges"
    - "sparse recovery for orthogonal polynomial transforms"
    - "automl a survey of the state of the art"
    - "meta learning a survey"
    - "recursive feature generation for knowledge based learning"
    - "taking human out of learning applications a survey on automated machine learning"
    - "autotune a derivative free optimization framework for hyperparameter tuning"

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)