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: "fast damage recovery in robotics with the t resilience algorithm"
  Abstract: Damage recovery is critical for autonomous robots that need to operate for a long time without assistance. Most current methods are complex and costly because they require anticipating potential damage in order to have a contingency plan ready. As an alternative, we introduce the T-resilience algorithm, a new algorithm that allows robots to quickly and autonomously discover compensatory behavior in unanticipated situations. This algorithm equips the robot with a self-model and discovers new behavior by learning to avoid those that perform differently in the self-model and in reality. Our algorithm thus does not identify the damaged parts but it implicitly searches for efficient behavior that does not use them. We evaluate the T-resilience algorithm on a hexapod robot that needs to adapt to leg removal, broken legs and motor failures; we compare it to stochastic local search, policy gradient and the self-modeling algorithm proposed by Bongard et al. The behavior of the robot is assessed on-board thanks to an RGB-D sensor and a SLAM algorithm. Using only 25 tests on the robot and an overall running time of 20 min, T-resilience consistently leads to substantially better results than the other approaches.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for single shot diagnosis and adaptation in damaged robots"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "care cooperative autonomy for resilience and efficiency of robot teams for complete coverage of unknown environments under robot failures"):
  with members:
    - "multi robot dubins coverage with autonomous surface vehicles"
    - "efficient multi robot coverage of a known environment"

Connected through hyperedge HE-3 (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-4 (induced by source paper "reset free trial and error learning for robot damage recovery"):
  with members:
    - "patchwork kriging for large scale gaussian process regression"
    - "quality and diversity optimization a unifying modular framework"
    - "limbo a fast and flexible library for bayesian optimization"
    - "reset free guided policy search efficient deep reinforcement learning with stochastic initial states"
    - "robots that can adapt like animals"
    - "trust region policy optimization"
    - "multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-5 (induced by source paper "map based multi policy reinforcement learning enhancing adaptability of robots by deep reinforcement learning"):
  with members:
    - "reset free trial and error learning for robot damage recovery"
    - "evolving deep neural networks"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "robust adversarial reinforcement learning"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "feudal networks for hierarchical reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-6 (induced by source paper "learning agent s spatial configuration from sensorimotor invariants"):
  with members:
    - "a non linear approach to space dimension perception by a naive agent"
    - "learning an internal representation of the end effector configuration space"
    - "where do goals come from a generic approach to autonomous goal system development"
    - "space as an invention of biological organisms"
    - "active learning of inverse models with intrinsically motivated goal exploration in robots"
    - "estimating uncertain spatial relationships in robotics"

Connected through hyperedge HE-7 (induced by source paper "evolvability signatures of generative encodings beyond standard performance benchmarks"):
  with members:
    - "multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation"
    - "evolvability is inevitable increasing evolvability without the pressure to adapt"

Connected through hyperedge HE-8 (induced by source paper "learning to walk in every direction"):
  with members:
    - "evolvability signatures of generative encodings beyond standard performance benchmarks"
    - "crossing the reality gap a short introduction to the transferability approach"

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)