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: "gradient free policy architecture search and adaptation"
  Abstract: We develop a method for policy architecture search and adaptation via gradient-free optimization which can learn to perform autonomous driving tasks. By learning from both demonstration and environmental reward we develop a model that can learn with relatively few early catastrophic failures. We first learn an architecture of appropriate complexity to perceive aspects of world state relevant to the expert demonstration, and then mitigate the effect of domain-shift during deployment by adapting a policy demonstrated in a source domain to rewards obtained in a target environment. We show that our approach allows safer learning than baseline methods, offering a reduced cumulative crash metric over the agent's lifetime as it learns to drive in a realistic simulated environment.

Connected through hyperedge HE-1 (induced by source paper "risk averse robust adversarial reinforcement learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "virtual to real reinforcement learning for autonomous driving"
    - "parameter space noise for exploration"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-2 (induced by source paper "variational adversarial active learning"):
  with members:
    - "uncertainty guided continual learning with bayesian neural networks"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cost sensitive active learning for intracranial hemorrhage detection"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "adversarial sampling for active learning"
    - "cost effective active learning for melanoma segmentation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "adversarial representation active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "large scale adversarial representation learning"
    - "rethinking deep active learning using unlabeled data at model training"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "variational adversarial active learning"
    - "high fidelity image generation with fewer labels"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generative adversarial active learning"

Connected through hyperedge HE-4 (induced by source paper "driving policy transfer via modularity and abstraction"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "virtual to real reinforcement learning for autonomous driving"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "mutual alignment transfer learning"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-5 (induced by source paper "evolutionary stochastic gradient descent for optimization of deep neural networks"):
  with members:
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "evolving deep neural networks"
    - "progressive neural architecture search"
    - "es is more than just a traditional finite difference approximator"
    - "regularizing and optimizing lstm language models"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "conditional affordance learning for driving in urban environments"):
  with members:
    - "driving policy transfer via modularity and abstraction"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "carla an open urban driving simulator"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "end to end driving via conditional imitation learning"
    - "playing for data ground truth from computer games"
    - "end to end learning of driving models from large scale video datasets"
    - "fully convolutional instance aware semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "es is more than just a traditional finite difference approximator"):
  with members:
    - "parameter space noise for exploration"
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "trust region policy optimization"
    - "evolvability is inevitable increasing evolvability without the pressure to adapt"

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)