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: "tabular benchmarks for joint architecture and hyperparameter optimization"
  Abstract: Due to the high computational demands executing a rigorous comparison between hyperparameter optimization (HPO) methods is often cumbersome. The goal of this paper is to facilitate a better empirical evaluation of HPO methods by providing benchmarks that are cheap to evaluate, but still represent realistic use cases. We believe these benchmarks provide an easy and efficient way to conduct reproducible experiments for neural hyperparameter search. Our benchmarks consist of a large grid of configurations of a feed forward neural network on four different regression datasets including architectural hyperparameters and hyperparameters concerning the training pipeline. Based on this data, we performed an in-depth analysis to gain a better understanding of the properties of the optimization problem, as well as of the importance of different types of hyperparameters. Second, we exhaustively compared various different state-of-the-art methods from the hyperparameter optimization literature on these benchmarks in terms of performance and robustness.

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 "nas bench 201 extending the scope of reproducible neural architecture search"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "one shot neural architecture search via self evaluated template network"
    - "searching for a robust neural architecture in four gpu hours"
    - "dropblock a regularization method for convolutional networks"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (induced by source paper "nas bench 102 extending the scope of reproducible neural architecture search"):
  with members:
    - "nas bench 1shot1 benchmarking and dissecting one shot neural architecture search"
    - "random search and reproducibility for neural architecture search"
    - "one shot neural architecture search via self evaluated template network"
    - "searching for a robust neural architecture in four gpu hours"
    - "dropblock a regularization method for convolutional networks"
    - "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-4 (induced by source paper "nas bench 101 towards reproducible neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "enumerating unique computational graphs via an iterative graph invariant"
    - "the evolved transformer"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "darts differentiable architecture search"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-5 (induced by source paper "best practices for scientific research on neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "autogan neural architecture search for generative adversarial networks"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "evaluating the search phase of neural architecture search"
    - "autodispnet improving disparity estimation with automl"
    - "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"

Connected through hyperedge HE-6 (induced by source paper "meta surrogate benchmarking for hyperparameter optimization"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesian optimization in alphago"
    - "on the state of the art of evaluation in neural language models"
    - "population based training of neural networks"
    - "xgboost a scalable tree boosting system"
    - "coco performance assessment"
    - "coco a platform for comparing continuous optimizers in a black box setting"
    - "non stochastic best arm identification and hyperparameter optimization"

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)