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: "reset free trial and error learning for robot damage recovery"
  Abstract: Abstract   The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In this situation, damage recovery can be seen as a Reinforcement Learning (RL) problem. However, the best RL algorithms for robotics require the robot and the environment to be reset to an initial state after each episode, that is, the robot is not learning autonomously. In addition, most of the RL methods for robotics do not scale well with complex robots (e.g., walking robots) and either cannot be used at all or take too long to converge to a solution (e.g., hours of learning). In this paper, we introduce a novel learning algorithm called “Reset-free Trial-and-Error” (RTE) that (1) breaks the complexity by pre-generating hundreds of possible behaviors with a dynamics simulator of the intact robot, and (2) allows complex robots to quickly recover from damage while completing their tasks and taking the environment into account. We evaluate our algorithm on a simulated wheeled robot, a simulated six-legged robot, and a real six-legged walking robot that are damaged in several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and whose objective is to reach a sequence of targets in an arena. Our experiments show that the robots can recover most of their locomotion abilities in an environment with obstacles, and without any human intervention.

Connected through hyperedge HE-1 (induced by source paper "scaling map elites to deep neuroevolution"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "adversarial policies attacking deep reinforcement learning"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "evolvability es scalable and direct optimization of evolvability"
    - "teacher algorithms for curriculum learning of deep rl in continuously parameterized environments"
    - "exploration by random network distillation"

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

Connected through hyperedge HE-4 (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"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "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-5 (induced by source paper "unsupervised learning and exploration of reachable outcome space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "from exploration to control learning object manipulation skills through novelty search and local adaptation"
    - "visual reinforcement learning with imagined goals"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "hierarchical behavioral repertoires with unsupervised descriptors"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for single shot diagnosis and adaptation in damaged robots"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "lstm a search space odyssey"
    - "robust adversarial reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "control of a quadrotor with reinforcement learning"
    - "map based multi policy reinforcement learning enhancing adaptability of robots by deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-7 (induced by source paper "from exploration to control learning object manipulation skills through novelty search and local adaptation"):
  with members:
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "behavioural repertoire via generative adversarial policy networks"
    - "quality and diversity optimization a unifying modular framework"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "using centroidal voronoi tessellations to scale up the multi dimensional archive of phenotypic elites algorithm"
    - "micro data learning the other end of the spectrum"
    - "robots that can adapt like animals"

Connected through hyperedge HE-8 (induced by source paper "automated shapeshifting for function recovery in damaged robots"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "interoceptive robustness through environment mediated morphological development"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "a minimal developmental model can increase evolvability in soft robots"
    - "robots that can adapt like animals"
    - "multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation"

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)