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: "bohb robust and efficient hyperparameter optimization at scale"
  Abstract: Modern deep learning methods are very sensitive to many hyperparameters, and, due to the long training times of state-of-the-art models, vanilla Bayesian hyperparameter optimization is typically computationally infeasible. On the other hand, bandit-based configuration evaluation approaches based on random search lack guidance and do not converge to the best configurations as quickly. Here, we propose to combine the benefits of both Bayesian optimization and bandit-based methods, in order to achieve the best of both worlds: strong anytime performance and fast convergence to optimal configurations. We propose a new practical state-of-the-art hyperparameter optimization method, which consistently outperforms both Bayesian optimization and Hyperband on a wide range of problem types, including high-dimensional toy functions, support vector machines, feed-forward neural networks, Bayesian neural networks, deep reinforcement learning, and convolutional neural networks. Our method is robust and versatile, while at the same time being conceptually simple and easy to implement.

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 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"
    - "tabular benchmarks for joint architecture and hyperparameter optimization"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-3 (induced by source paper "random search and reproducibility for neural architecture search"):
  with members:
    - "learnable embedding space for efficient neural architecture compression"
    - "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"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "botorch programmable bayesian optimization in pytorch"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "practical multi fidelity bayesian optimization for hyperparameter tuning"
    - "subspace inference for bayesian deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "pyro deep universal probabilistic programming"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"

Connected through hyperedge HE-5 (induced by source paper "toward finding the global optimal of adversarial examples"):
  with members:
    - "sign opt a query efficient hard label adversarial attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "robust decision trees against adversarial examples"
    - "enhancing gradient based attacks with symbolic intervals"
    - "ml loo detecting adversarial examples with feature attribution"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "mixtrain scalable training of formally robust neural networks"
    - "genattack practical black box attacks with gradient free optimization"

Connected through hyperedge HE-6 (induced by source paper "flight controller synthesis via deep reinforcement learning"):
  with members:
    - "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"
    - "neuroflight next generation flight control firmware"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "learning dexterous in hand manipulation"
    - "reinforcement learning for uav attitude control"
    - "end to end analysis and design of a drone flight controller"
    - "deep reinforcement learning that matters"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-7 (induced by source paper "deep neural networks for choice analysis architectural design with alternative specific utility functions"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "size independent sample complexity of neural networks"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "learning functions when is deep better than shallow"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "mixed variable bayesian optimization"):
  with members:
    - "high dimensional bayesian optimization via additive models with overlapping groups"
    - "hyperparameter optimization a spectral approach"
    - "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"

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)