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: "when random search is not enough sample efficient and noise robust blackbox optimization of rl policies"
  Abstract: Interest in derivative-free optimization (DFO) and "evolutionary strategies" (ES) has recently surged in the Reinforcement Learning (RL) community, with growing evidence that they match state of the art methods for policy optimization tasks. However, blackbox DFO methods suffer from high sampling complexity since they require a substantial number of policy rollouts for reliable updates. They can also be very sensitive to noise in the rewards, actuators or the dynamics of the environment. In this paper we propose to replace the standard ES derivative-free paradigm for RL based on simple reward-weighted averaged random perturbations for policy updates, that has recently become a subject of voluminous research, by an algorithm where gradients of blackbox RL functions are estimated via regularized regression methods. In particular, we propose to use L1/L2 regularized regression-based gradient estimation to exploit sparsity and smoothness, as well as LP decoding techniques for handling adversarial stochastic and deterministic noise. Our methods can be naturally aligned with sliding trust region techniques for efficient samples reuse to further reduce sampling complexity. This is not the case for standard ES methods requiring independent sampling in each epoch. We show that our algorithms can be applied in locomotion tasks, where training is conducted in the presence of substantial noise, e.g. for learning in sim transferable stable walking behaviors for quadruped robots or training quadrupeds how to follow a path. We further demonstrate our methods on several $\mathrm{OpenAI}$ $\mathrm{Gym}$ $\mathrm{Mujoco}$ RL tasks. We manage to train effective policies even if up to $25\%$ of all measurements are arbitrarily corrupted, where standard ES methods produce sub-optimal policies or do not manage to learn at all. Our empirical results are backed by theoretical guarantees.

Connected through hyperedge HE-1 (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-2 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy"):
  with members:
    - "provably robust blackbox optimization for reinforcement learning"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "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-3 (induced by source paper "how to pick the domain randomization parameters for sim to real transfer of reinforcement learning policies"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "policies modulating trajectory generators"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "supervised policy update"
    - "learning dexterous in hand manipulation"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning to simulate"

Connected through hyperedge HE-4 (induced by source paper "structured monte carlo sampling for nonisotropic distributions via determinantal point processes"):
  with members:
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "orthogonal random features"
    - "benchmarking deep reinforcement learning for continuous control"
    - "efficient sampling for k determinantal point processes"
    - "adam a method for stochastic optimization"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "qibullet a bullet based simulator for the pepper and nao robots"):
  with members:
    - "self supervised body image acquisition using a deep neural network for sensorimotor prediction"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "sim to real learning agile locomotion for quadruped robots"
    - "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"
    - "webotstm professional mobile robot simulation"

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)