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: "black box data efficient policy search for robotics"
  Abstract: The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this optimization can be tractable only if analytical, gradient-based algorithms are used; however, these algorithms require using specific families of reward functions and policies, which greatly limits the flexibility of the overall approach. In this paper, we introduce a novel model-based RL algorithm, called Black-DROPS (Black-box Data-efficient RObot Policy Search) that: (1) does not impose any constraint on the reward function or the policy (they are treated as black-boxes), (2) is as data-efficient as the state-of-the-art algorithm for data-efficient RL in robotics, and (3) is as fast (or faster) than analytical approaches when several cores are available. The key idea is to replace the gradient-based optimization algorithm with a parallel, black-box algorithm that takes into account the model uncertainties. We demonstrate the performance of our new algorithm on two standard control benchmark problems (in simulation) and a low-cost robotic manipulator (with a real robot).

Connected through hyperedge HE-1 (induced by source paper "improving deep neuroevolution via deep innovation protection"):
  with members:
    - "weight agnostic neural networks"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "scalable co optimization of morphology and control in embodied machines"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-2 (induced by source paper "fast online adaptation in robotics through meta learning embeddings of simulated priors"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "reset free trial and error learning for robot damage recovery"
    - "on first order meta learning algorithms"
    - "multi objective model based policy search for data efficient learning with sparse rewards"
    - "efficient transfer learning and online adaptation with latent variable models for continuous control"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"

Connected through hyperedge HE-3 (induced by source paper "adaptive prior selection for repertoire based online adaptation in robotics"):
  with members:
    - "dynamics aware unsupervised discovery of skills"
    - "reset free trial and error learning for robot damage recovery"
    - "multi objective model based policy search for data efficient learning with sparse rewards"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "a survey on policy search algorithms for learning robot controllers in a handful of trials"
    - "patchwork kriging for large scale gaussian process regression"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "emergence of locomotion behaviours in rich environments"

Connected through hyperedge HE-4 (induced by source paper "deep neuroevolution of recurrent and discrete world models"):
  with members:
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "evolving deep neural networks"
    - "autoencoder augmented neuroevolution for visual doom playing"

Connected through hyperedge HE-5 (induced by source paper "evolving embodied intelligence from materials to machines"):
  with members:
    - "interoceptive robustness through environment mediated morphological development"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "hierarchical behavioral repertoires with unsupervised descriptors"
    - "using parameterized black box priors to scale up model based policy search for robotics"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"
    - "evolutionary approaches to expensive optimisation"

Connected through hyperedge HE-6 (induced by source paper "feedback based fabric strip folding"):
  with members:
    - "static stability of robotic fabric strip folding"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "learning based feedback controller for deformable object manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "deep reinforcement learning to acquire navigation skills for wheel legged robots in complex environments"
    - "folding deformable objects using predictive simulation and trajectory optimization"

Connected through hyperedge HE-7 (induced by source paper "probabilistic model learning and long term prediction for contact rich manipulation tasks"):
  with members:
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "learning contact rich manipulation skills with guided policy search"

Connected through hyperedge HE-8 (induced by source paper "pipps flexible model based policy search robust to the curse of chaos"):
  with members:
    - "variational inference for monte carlo objectives"
    - "gradient estimation using stochastic computation graphs"
    - "pegasus a policy search method for large mdps and pomdps"

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.RO(Robotics)