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: "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
  Abstract: Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related physical effects with first-order modeling, traditional control methods often result in brittle and inaccurate controllers, which have to be manually tuned. Reinforcement learning (RL) methods have been demonstrated to be capable of learning controllers in such environments from autonomous interaction with the environment, but running RL algorithms in the real world poses sample efficiency and safety challenges. Moreover, in practical real-world settings we cannot assume access to perfect state information or dense reward signals. In this paper, we consider a variety of difficult industrial insertion tasks with visual inputs and different natural reward specifications, namely sparse rewards and goal images. We show that methods that combine RL with prior information, such as classical controllers or demonstrations, can solve these tasks from a reasonable amount of real-world interaction.

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"
    - "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"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-2 (induced by source paper "learning contact rich manipulation tasks with rigid position controlled robots learning to force control"):
  with members:
    - "learning variable impedance control for contact sensitive tasks"
    - "variable impedance control in end effector space an action space for reinforcement learning in contact rich tasks"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"
    - "a framework for robot manipulation skill formalism meta learning and adaptive control"
    - "residual reinforcement learning for robot control"

Connected through hyperedge HE-3 (induced by source paper "applying depth sensing to automated surgical manipulation with a da vinci robot"):
  with members:
    - "autonomous tissue manipulation via surgical robot using learning based model predictive control"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "open sourced reinforcement learning environments for surgical robotics"
    - "toward synergic learning for autonomous manipulation of deformable tissues via surgical robots an approximate q learning approach"
    - "super a surgical perception framework for endoscopic tissue manipulation with surgical robotics"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "assembly robots with optimized control stiffness through reinforcement learning"):
  with members:
    - "fast skill learning for variable compliance robotic assembly"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "a practical approach to insertion with variable socket position using deep reinforcement learning"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "progressive neural architecture search"
    - "deep reinforcement learning for high precision assembly tasks"
    - "sim to real robot learning from pixels with progressive nets"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-5 (induced by source paper "visual geometric skill inference by watching human demonstration"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "online object and task learning via human robot interaction"
    - "robot eye hand coordination learning by watching human demonstrations a task function approximation approach"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "neural motifs scene graph parsing with global context"

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)