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: "simple random search provides a competitive approach to reinforcement learning"
  Abstract: A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions. We dispel such beliefs by introducing a random search method for training static, linear policies for continuous control problems, matching state-of-the-art sample efficiency on the benchmark MuJoCo locomotion tasks. Our method also finds a nearly optimal controller for a challenging instance of the Linear Quadratic Regulator, a classical problem in control theory, when the dynamics are not known. Computationally, our random search algorithm is at least 15 times more efficient than the fastest competing model-free methods on these benchmarks. We take advantage of this computational efficiency to evaluate the performance of our method over hundreds of random seeds and many different hyperparameter configurations for each benchmark task. Our simulations highlight a high variability in performance in these benchmark tasks, suggesting that commonly used estimations of sample efficiency do not adequately evaluate the performance of RL algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-3 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning"):
  with members:
    - "es maml simple hessian free meta learning"
    - "active domain randomization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "varibad a very good method for bayes adaptive deep rl via meta learning"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "norml no reward meta learning"
    - "learning generalizable locomotion skills with hierarchical reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-4 (induced by source paper "policy evaluation networks"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "the value function polytope in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "value prediction network"
    - "a distributional perspective on reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "population based training of neural networks"

Connected through hyperedge HE-5 (induced by source paper "maximizing the total reward via reward tweaking"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "on first order meta learning algorithms"
    - "on learning intrinsic rewards for policy gradient methods"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning with chromatic networks for compact architecture search"):
  with members:
    - "non differentiable supervised learning with evolution strategies and hybrid methods"
    - "exploring randomly wired neural networks for image recognition"
    - "policies modulating trajectory generators"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "playing atari with six neurons"
    - "neural architecture search with reinforcement learning"
    - "compression of neural machine translation models via pruning"

Connected through hyperedge HE-7 (induced by source paper "post estimation smoothing a simple baseline for learning with side information"):
  with members:
    - "predicting 3d human dynamics from video"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "semi supervised deep kernel learning regression with unlabeled data by minimizing predictive variance"
    - "learning 3d human dynamics from video"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "end to end recovery of human shape and pose"
    - "learning 3d human pose from structure and motion"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-8 (induced by source paper "observational overfitting in reinforcement learning"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "generalization in reinforcement learning with selective noise injection and information bottleneck"
    - "on the generalization gap in reparameterizable reinforcement learning"
    - "weight agnostic neural networks"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"

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)