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: "supervision via competition robot adversaries for learning tasks"
  Abstract: There has been a recent paradigm shift in robotics to data-driven learning for planning and control. Due to large number of experiences required for training, most of these approaches use a self-supervised paradigm: using sensors to measure success/failure. However, in most cases, these sensors provide weak supervision at best. In this work, we propose an adversarial learning framework that pits an adversary against the robot learning the task. In an effort to defeat the adversary, the original robot learns to perform the task with more robustness leading to overall improved performance. We show that this adversarial framework forces the the robot to learn a better grasping model in order to overcome the adversary. By grasping 82% of presented novel objects compared to 68% without an adversary, we demonstrate the utility of creating adversaries. We also demonstrate via experiments that having robots in adversarial setting might be a better learning strategy as compared to having collaborative multiple robots.

Connected through hyperedge HE-1 (induced by source paper "deep adversarial reinforcement learning for object disentangling"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "dota 2 with large scale deep reinforcement learning"
    - "risk averse robust adversarial reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "adversarial attacks on neural network policies"
    - "automatic goal generation for reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "data efficient image recognition with contrastive predictive coding"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "contrastive multiview coding"
    - "momentum contrast for unsupervised visual representation learning"
    - "large scale adversarial representation learning"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "fast autoaugment"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-4 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "marathon environments multi agent continuous control benchmarks in a modern video game engine"
    - "emergent coordination through competition"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "habitat a platform for embodied ai research"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-5 (induced by source paper "risk averse robust adversarial reinforcement learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "virtual to real reinforcement learning for autonomous driving"
    - "gradient free policy architecture search and adaptation"
    - "parameter space noise for exploration"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "pyrobot an open source robotics framework for research and benchmarking"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "learning agile and dynamic motor skills for legged robots"
    - "habitat a platform for embodied ai research"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "gibson env real world perception for embodied agents"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "robot learning in homes improving generalization and reducing dataset bias"

Connected through hyperedge HE-7 (induced by source paper "improving data efficiency of self supervised learning for robotic grasping"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "end to end learning of semantic grasping"
    - "sim to real robot learning from pixels with progressive nets"
    - "deep spatial autoencoders for visuomotor learning"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"
    - "real time grasp detection using convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "robot learning via human adversarial games"):
  with members:
    - "deep reinforcement learning from policy dependent human feedback"
    - "shared autonomy via deep reinforcement learning"
    - "explore exploit or listen combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds"
    - "interactive learning from policy dependent human feedback"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "learning to poke by poking experiential learning of intuitive physics"

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)