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 multi start strategies for local search algorithms"
  Abstract: Local search algorithms applied to optimization problems often suffer from getting trapped in a local optimum. The common solution for this deficiency is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resources (in particular, processing time) to the instances depending on their behavior. Hence, a multi-start strategy has to decide (dynamically) when to allocate additional resources to a particular instance and when to start new instances. In this paper we propose multi-start strategies motivated by works on multi-armed bandit problems and Lipschitz optimization with an unknown constant. The strategies continuously estimate the potential performance of each algorithm instance by supposing a convergence rate of the local search algorithm up to an unknown constant, and in every phase allocate resources to those instances that could converge to the optimum for a particular range of the constant. Asymptotic bounds are given on the performance of the strategies. In particular, we prove that at most a quadratic increase in the number of times the target function is evaluated is needed to achieve the performance of a local search algorithm started from the attraction region of the optimum. Experiments are provided using SPSA (Simultaneous Perturbation Stochastic Approximation) and kmeans as local search algorithms, and the results indicate that the proposed strategies work well in practice, and, in all cases studied, need only logarithmically more evaluations of the target function as opposed to the theoretically suggested quadratic increase.

Connected through hyperedge HE-1 (induced by source paper "taking human out of learning applications a survey on automated machine learning"):
  with members:
    - "meta learning a survey"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "efficient progressive neural architecture search"
    - "darts differentiable architecture search"
    - "transfer learning with neural automl"

Connected through hyperedge HE-2 (induced by source paper "deep learning computational aspects"):
  with members:
    - "global explanations of neural networks mapping the landscape of predictions"
    - "tvm end to end optimization stack for deep learning"
    - "towards explainable deep learning for credit lending a case study"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "efficient natural language response suggestion for smart reply"
    - "attention based extraction of structured information from street view imagery"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-3 (induced by source paper "hyperband a novel bandit based approach to hyperparameter optimization"):
  with members:
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "thoughts on massively scalable gaussian processes"
    - "simple regret for infinitely many armed bandits"
    - "least squares revisited scalable approaches for multi class prediction"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "convolutional neural networks applied to house numbers digit classification"
    - "fast cross validation via sequential testing"

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)