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: "conditioning by adaptive sampling for robust design"
  Abstract: We present a new method for design problems wherein the goal is to maximize or specify the value of one or more properties of interest. For example, in protein design, one may wish to find the protein sequence that maximizes fluorescence. We assume access to one or more, potentially black box, stochastic "oracle" predictive functions, each of which maps from input (e.g., protein sequences) design space to a distribution over a property of interest (e.g. protein fluorescence). At first glance, this problem can be framed as one of optimizing the oracle(s) with respect to the input. However, many state-of-the-art predictive models, such as neural networks, are known to suffer from pathologies, especially for data far from the training distribution. Thus we need to modulate the optimization of the oracle inputs with prior knowledge about what makes `realistic' inputs (e.g., proteins that stably fold). Herein, we propose a new method to solve this problem, Conditioning by Adaptive Sampling, which yields state-of-the-art results on a protein fluorescence problem, as compared to other recently published approaches. Formally, our method achieves its success by using model-based adaptive sampling to estimate the conditional distribution of the input sequences given the desired properties.

Connected through hyperedge HE-1 (induced by source paper "differentiating the black box optimization with local generative surrogates"):
  with members:
    - "automatic posterior transformation for likelihood free inference"
    - "high dimensional bayesian optimization via supervised dimension reduction"
    - "measuring the intrinsic dimension of objective landscapes"
    - "pyro deep universal probabilistic programming"
    - "learning to simulate"
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"

Connected through hyperedge HE-2 (induced by source paper "explainable machine learning for scientific insights and discoveries"):
  with members:
    - "a rate distortion framework for explaining neural network decisions"
    - "explaining explanations in ai"
    - "neural guided symbolic regression with semantic prior"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "causality for machine learning"
    - "interpretations are useful penalizing explanations to align neural networks with prior knowledge"
    - "the challenge of crafting intelligible intelligence"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"

Connected through hyperedge HE-3 (induced by source paper "bayesian variational autoencoders for unsupervised out of distribution detection"):
  with members:
    - "a cold approach to generating optimal samples"
    - "all smiles variational autoencoder"
    - "detecting semantic anomalies"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"
    - "efficient gan based anomaly detection"
    - "uncertainty in the variational information bottleneck"
    - "ganomaly semi supervised anomaly detection via adversarial training"

Connected through hyperedge HE-4 (induced by source paper "robust regression for safe exploration in control"):
  with members:
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "control regularization for reduced variance reinforcement learning"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "neural lander stable drone landing control using learned dynamics"
    - "spectral normalization for generative adversarial networks"
    - "measuring and regularizing networks in function space"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-5 (induced by source paper "model inversion networks for model based optimization"):
  with members:
    - "data efficient learning of morphology and controller for a microrobot"
    - "attentive neural processes"
    - "neural processes"
    - "conditional neural processes"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "large scale gan training for high fidelity natural image synthesis"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "parallel wavenet fast high fidelity speech synthesis"

Connected through hyperedge HE-6 (induced by source paper "a view of estimation of distribution algorithms through the lens of expectation maximization"):
  with members:
    - "simple random search provides a competitive approach to reinforcement learning"
    - "new insights and perspectives on the natural gradient method"
    - "fast variational inference in the conjugate exponential family"
    - "path integral policy improvement with covariance matrix adaptation"

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)