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: "structured evolution with compact architectures for scalable policy optimization"
  Abstract: We present a new method of blackbox optimization via gradient approximation with the use of structured random orthogonal matrices, providing more accurate estimators than baselines and with provable theoretical guarantees. We show that this algorithm can be successfully applied to learn better quality compact policies than those using standard gradient estimation techniques. The compact policies we learn have several advantages over unstructured ones, including faster training algorithms and faster inference. These benefits are important when the policy is deployed on real hardware with limited resources. Further, compact policies provide more scalable architectures for derivative-free optimization (DFO) in high-dimensional spaces. We show that most robotics tasks from the OpenAI Gym can be solved using neural networks with less than 300 parameters, with almost linear time complexity of the inference phase, with up to 13x fewer parameters relative to the Evolution Strategies (ES) algorithm introduced by Salimans et al. (2017). We do not need heuristics such as fitness shaping to learn good quality policies, resulting in a simple and theoretically motivated training mechanism.

Connected through hyperedge HE-1 (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-2 (induced by source paper "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "regularizing activation distribution for training binarized deep networks"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "matrix and tensor decompositions for training binary neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "projection convolutional neural networks for 1 bit cnns via discrete back propagation"
    - "training and inference with integers in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "addressing function approximation error in actor critic methods"
    - "policy search in continuous action domains an overview"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"

Connected through hyperedge HE-4 (induced by source paper "population guided parallel policy search for reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "self imitation learning"
    - "coordinated exploration in concurrent reinforcement learning"
    - "evolution guided policy gradient in reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "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-6 (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"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "simple random search provides a competitive approach to reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "es maml simple hessian free meta learning"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "norml no reward meta learning"
    - "evolvability es scalable and direct optimization of evolvability"
    - "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"
    - "deep neuroevolution of recurrent and discrete world models"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "meta learning by the baldwin effect"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning with chromatic networks"):
  with members:
    - "weight agnostic neural networks"
    - "exploring randomly wired neural networks for image recognition"
    - "learning sparse networks using targeted dropout"
    - "policies modulating trajectory generators"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "playing atari with six neurons"
    - "efficient neural architecture search via parameter sharing"

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)