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: "emergence of locomotion behaviours in rich environments"
  Abstract: The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully hand-design the reward function to encourage a particular solution, or to derive it from demonstration data. In this paper explore how a rich environment can help to promote the learning of complex behavior. Specifically, we train agents in diverse environmental contexts, and find that this encourages the emergence of robust behaviours that perform well across a suite of tasks. We demonstrate this principle for locomotion -- behaviours that are known for their sensitivity to the choice of reward. We train several simulated bodies on a diverse set of challenging terrains and obstacles, using a simple reward function based on forward progress. Using a novel scalable variant of policy gradient reinforcement learning, our agents learn to run, jump, crouch and turn as required by the environment without explicit reward-based guidance. A visual depiction of highlights of the learned behavior can be viewed following this https URL .

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"

Connected through hyperedge HE-2 (induced by source paper "learning to walk in the real world with minimal human effort"):
  with members:
    - "safety guided deep reinforcement learning via online gaussian process estimation"
    - "solving rubik s cube with a robot hand"
    - "generalizing from a few environments in safety critical reinforcement learning"
    - "data efficient reinforcement learning for legged robots"
    - "deepgait planning and control of quadrupedal gaits using deep reinforcement learning"
    - "norml no reward meta learning"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "learning agile and dynamic motor skills for legged robots"

Connected through hyperedge HE-3 (induced by source paper "balance between efficient and effective learning dense2sparse reward shaping for robot manipulation with environment uncertainty"):
  with members:
    - "contextual imagined goals for self supervised robotic learning"
    - "reward shaping via meta learning"
    - "dynamical distance learning for unsupervised and semi supervised skill discovery"
    - "motion perception in reinforcement learning with dynamic objects"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning with perturbed rewards"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-4 (induced by source paper "particle based adaptive discretization for continuous control using deep reinforcement learning"):
  with members:
    - "discretizing continuous action space for on policy optimization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sfv reinforcement learning of physical skills from videos"
    - "soft actor critic algorithms and applications"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "enhanced poet open ended reinforcement learning through unbounded invention of learning challenges and their solutions"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "go explore a new approach for hard exploration problems"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "alphastar an evolutionary computation perspective"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"

Connected through hyperedge HE-6 (induced by source paper "curriculum in gradient based meta reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "skew fit state covering self supervised reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "mame model agnostic meta exploration"
    - "learning domain randomization distributions for transfer of locomotion policies"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"

Connected through hyperedge HE-7 (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-8 (induced by source paper "dynamic experience replay"):
  with members:
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "distributed prioritized experience replay"
    - "distributed distributional deterministic policy gradients"
    - "a learning framework for high precision industrial assembly"
    - "distributed deep reinforcement learning learn how to play atari games in 21 minutes"
    - "hindsight experience replay"
    - "deep reinforcement learning for high precision assembly tasks"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"

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.AI(Artificial Intelligence)