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: "multi agent manipulation via locomotion using hierarchical sim2real"
  Abstract: Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit manipulation behaviors using a reinforcement learning (RL) approach. Our method hinges on the use of hierarchical sim2real -- a simulated environment is used to learn low-level goal-reaching skills, which are then used as the action space for a high-level RL controller, also trained in simulation. The full hierarchical policy is then transferred to the real world in a zero-shot fashion. The application of domain randomization during training enables the learned behaviors to generalize to real-world settings, while the use of hierarchy provides a modular paradigm for learning and transferring increasingly complex behaviors. We evaluate our method on a number of real-world tasks, including coordinated object manipulation in a multi-agent setting. See videos at this https URL

Connected through hyperedge HE-1 (induced by source paper "rapidly adaptable legged robots via evolutionary meta learning"):
  with members:
    - "es maml simple hessian free meta learning"
    - "active domain randomization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "varibad a very good method for bayes adaptive deep rl via meta learning"
    - "norml no reward meta learning"
    - "learning generalizable locomotion skills with hierarchical reinforcement learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "meta reinforcement learning for sim to real domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "intrinsic motivation for encouraging synergistic behavior"):
  with members:
    - "learning gentle object manipulation with curiosity driven deep reinforcement learning"
    - "self supervised exploration via disagreement"
    - "efficient bimanual manipulation using learned task schemas"
    - "emergent coordination through competition"
    - "large scale study of curiosity driven learning"
    - "emergence of structured behaviors from curiosity based intrinsic motivation"
    - "diversity is all you need learning skills without a reward function"
    - "learning to adapt meta learning for model based control"

Connected through hyperedge HE-3 (induced by source paper "brpo batch residual policy optimization"):
  with members:
    - "benchmarking batch deep reinforcement learning algorithms"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "residual policy learning"
    - "natural gradient deep q learning"
    - "off policy deep reinforcement learning without exploration"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "residual reinforcement learning for robot control"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "algaedice policy gradient from arbitrary experience"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "imitation learning via off policy distribution matching"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "a theory of regularized markov decision processes"
    - "a kernel loss for solving the bellman equation"
    - "breaking the curse of horizon infinite horizon off policy estimation"

Connected through hyperedge HE-5 (induced by source paper "why does hierarchy sometimes work so well in reinforcement learning"):
  with members:
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "rainbow combining improvements in deep reinforcement learning"
    - "parameter space noise for exploration"
    - "meta learning shared hierarchies"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"
    - "noisy networks for exploration"

Connected through hyperedge HE-6 (induced by source paper "imitation learning via off policy distribution matching"):
  with members:
    - "imitation learning as f divergence minimization"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"
    - "large scale gan training for high fidelity natural image synthesis"
    - "mine mutual information neural estimation"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "scalable sim to real transfer of soft robot designs"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "automated shapeshifting for function recovery in damaged robots"
    - "non smooth newton methods for deformable multi body dynamics"
    - "learning agile and dynamic motor skills for legged robots"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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)