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: "a survey on policy search algorithms for learning robot controllers in a handful of trials"
  Abstract: Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning through active inference"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "deep active inference as variational policy gradients"
    - "a unified bellman optimality principle combining reward maximization and empowerment"
    - "deep dynamics models for learning dexterous manipulation"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "efficient exploration via state marginal matching"
    - "dream to control learning behaviors by latent imagination"

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"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

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"
    - "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"
    - "quality and diversity optimization a unifying modular framework"

Connected through hyperedge HE-4 (induced by source paper "efficient robotic task generalization using deep model fusion reinforcement learning"):
  with members:
    - "hierarchical reinforcement learning framework towards multi agent navigation"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "a learning framework for high precision industrial assembly"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "proximal policy optimization algorithms"
    - "generalizing skills with semi supervised reinforcement learning"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "human robot collaboration via deep reinforcement learning of real world interactions"):
  with members:
    - "openspiel a framework for reinforcement learning in games"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "shared autonomy via deep reinforcement learning"
    - "learning to walk via deep reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "sim to real robot learning from pixels with progressive nets"

Connected through hyperedge HE-6 (induced by source paper "classified regression for bayesian optimization robot learning with unknown penalties"):
  with members:
    - "gait learning for soft microrobots controlled by light fields"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-7 (induced by source paper "ppo cma proximal policy optimization with covariance matrix adaptation"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "unity a general platform for intelligent agents"
    - "soft actor critic algorithms and applications"
    - "policy search in continuous action domains an overview"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "intelligent middle level game control"
    - "maximum a posteriori policy optimisation"
    - "relative entropy regularized policy iteration"

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)