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: "bayesian optimization with automatic prior selection for data efficient direct policy search"
  Abstract: One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current situation. We tackle this problem by introducing a novel acquisition function, called Most Likely Expected Improvement (MLEI), that combines the likelihood of the priors and the expected improvement. We evaluate this new acquisition function on a transfer learning task for a 5-DOF planar arm and on a possibly damaged, 6-legged robot that has to learn to walk on flat ground and on stairs, with priors corresponding to different stairs and different kinds of damages. Our results show that MLEI effectively identifies and exploits the priors, even when there is no obvious match between the current situations and the priors.

Connected through hyperedge HE-1 (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"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-2 (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"
    - "emergence of locomotion behaviours in rich environments"
    - "quality and diversity optimization a unifying modular framework"

Connected through hyperedge HE-3 (induced by source paper "quality diversity for multi task optimization"):
  with members:
    - "adaptive prior selection for repertoire based online adaptation in robotics"
    - "procedural content generation through quality diversity"
    - "reset free trial and error learning for robot damage recovery"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "evolutionary multitasking for single objective continuous optimization benchmark problems performance metric and baseline results"
    - "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
    - "quality and diversity optimization a unifying modular framework"
    - "robots that can adapt like animals"

Connected through hyperedge HE-4 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "riemannian motion policies"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "learning dynamic robot to human object handover from human feedback"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"

Connected through hyperedge HE-5 (induced by source paper "scenario generalization of data driven imitation models in crowd simulation"):
  with members:
    - "theoretical perspective of deep domain adaptation"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "on deep domain adaptation some theoretical understandings"
    - "behavioral cloning from observation"
    - "the role of data driven priors in multi agent crowd trajectory estimation"
    - "imitating driver behavior with generative adversarial networks"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-6 (induced by source paper "a survey on policy search algorithms for learning robot controllers in a handful of trials"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "an algorithmic perspective on imitation learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "world models"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"

Connected through hyperedge HE-7 (induced by source paper "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"):
  with members:
    - "generating rescheduling knowledge using reinforcement learning in a cognitive architecture"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "a greedy approach to adapting the trace parameter for temporal difference learning"
    - "algorithms for multi armed bandit problems"

Connected through hyperedge HE-8 (induced by source paper "data efficient neuroevolution with kernel based surrogate models"):
  with members:
    - "improving exploration in evolution strategies for deep reinforcement learning via a population of novelty seeking agents"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "data efficient exploration optimization and modeling of diverse designs through surrogate assisted illumination"
    - "on the relationship between the openai evolution strategy and stochastic gradient descent"
    - "black box data efficient policy search for robotics"
    - "robots that can adapt like animals"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)