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: "asymmetric actor critic for image based robot learning"
  Abstract: Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, Robotics poses many challenges for RL, most notably training on a physical system can be expensive and dangerous, which has sparked significant interest in learning control policies using a physics simulator. While several recent works have shown promising results in transferring policies trained in simulation to the real world, they often do not fully utilize the advantage of working with a simulator. In this work, we exploit the full state observability in the simulator to train better policies which take as input only partial observations (RGBD images). We do this by employing an actor-critic training algorithm in which the critic is trained on full states while the actor (or policy) gets rendered images as input. We show experimentally on a range of simulated tasks that using these asymmetric inputs significantly improves performance. Finally, we combine this method with domain randomization and show real robot experiments for several tasks like picking, pushing, and moving a block. We achieve this simulation to real world transfer without training on any real world data.

Connected through hyperedge HE-1 (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-2 (induced by source paper "generalized hindsight for reinforcement learning"):
  with members:
    - "sub policy adaptation for hierarchical reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "deep drone racing learning agile flight in dynamic environments"
    - "visual reinforcement learning with imagined goals"
    - "soft actor critic algorithms and applications"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "hindsight experience replay"

Connected through hyperedge HE-3 (induced by source paper "attention privileged reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "state representation learning for control an overview"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "on the continuity of rotation representations in neural networks"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-4 (induced by source paper "visual task progress estimation with appearance invariant embeddings for robot control and planning"):
  with members:
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "self supervised correspondence in visuomotor policy learning"
    - "learning representations by maximizing mutual information across views"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning actionable representations from visual observations"
    - "playing hard exploration games by watching youtube"
    - "visual reinforcement learning with imagined goals"
    - "one shot imitation from observing humans via domain adaptive meta learning"

Connected through hyperedge HE-5 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-6 (induced by source paper "emergent tool use from multi agent autocurricula"):
  with members:
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "the tools challenge rapid trial and error learning in physical problem solving"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "structured agents for physical construction"
    - "exploration by random network distillation"

Connected through hyperedge HE-7 (induced by source paper "simulation based reinforcement learning for real world autonomous driving"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "model based reinforcement learning for atari"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the limits and potentials of deep learning for robotics"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-8 (induced by source paper "divis domain invariant visual servoing for collision free goal reaching"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "zero shot visual imitation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "speaker follower models for vision and language navigation"
    - "viser visual self regularization"
    - "building generalizable agents with a realistic and rich 3d environment"

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)