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: "hyperparameter optimization a spectral approach"
  Abstract: We give a simple, fast algorithm for hyperparameter optimization inspired by techniques from the analysis of Boolean functions. We focus on the high-dimensional regime where the canonical example is training a neural network with a large number of hyperparameters. The algorithm --- an iterative application of compressed sensing techniques for orthogonal polynomials --- requires only uniform sampling of the hyperparameters and is thus easily parallelizable. #R##N#Experiments for training deep neural networks on Cifar-10 show that compared to state-of-the-art tools (e.g., Hyperband and Spearmint), our algorithm finds significantly improved solutions, in some cases better than what is attainable by hand-tuning. In terms of overall running time (i.e., time required to sample various settings of hyperparameters plus additional computation time), we are at least an order of magnitude faster than Hyperband and Bayesian Optimization. We also outperform Random Search 8x. #R##N#Additionally, our method comes with provable guarantees and yields the first improvements on the sample complexity of learning decision trees in over two decades. In particular, we obtain the first quasi-polynomial time algorithm for learning noisy decision trees with polynomial sample complexity.

Connected through hyperedge HE-1 (induced by source paper "one shot neural architecture search via compressive sensing"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "learnable embedding space for efficient neural architecture compression"
    - "evaluating the search phase of neural architecture search"
    - "massively parallel hyperparameter tuning"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "efficient automatic meta optimization search for few shot learning"):
  with members:
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "regularized evolution for image classifier architecture search"
    - "object level representation learning for few shot image classification"
    - "efficient neural architecture search via parameter sharing"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "id3 learns juntas for smoothed product distributions"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "beyond the low degree algorithm mixtures of subcubes and their applications"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-4 (induced by source paper "mixed variable bayesian optimization"):
  with members:
    - "high dimensional bayesian optimization via additive models with overlapping groups"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "xgboost a scalable tree boosting system"
    - "importance weighted autoencoders"
    - "adam a method for stochastic optimization"
    - "analysis of boolean functions"

Connected through hyperedge HE-5 (induced by source paper "hyper parameter tuning under a budget constraint"):
  with members:
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "population based training of neural networks"
    - "neural optimizer search with reinforcement learning"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "top down induction of decision trees rigorous guarantees and inherent limitations"):
  with members:
    - "on the optimality of trees generated by id3"
    - "beyond the low degree algorithm mixtures of subcubes and their applications"
    - "extractor based time space lower bounds for learning"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "analysis of boolean functions"
    - "learning circuits with few negations"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "the average sensitivity of an intersection of half spaces"

Connected through hyperedge HE-7 (induced by source paper "reducing the search space for hyperparameter optimization using group sparsity"):
  with members:
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "parallel architecture and hyperparameter search via successive halving and classification"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "drmad distilling reverse mode automatic differentiation for optimizing hyperparameters of deep neural networks"
    - "scalable gradient based tuning of continuous regularization hyperparameters"
    - "non stochastic best arm identification and hyperparameter optimization"
    - "analysis of boolean functions"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"

Connected through hyperedge HE-8 (induced by source paper "gradientless descent high dimensional zeroth order optimization"):
  with members:
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "proximal policy optimization algorithms"
    - "practical black box attacks against machine learning"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"
    - "adaptive bound optimization for online convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)