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: "a practical approach to insertion with variable socket position using deep reinforcement learning"
  Abstract: Insertion is a challenging haptic and visual control problem with significant practical value for manufacturing. Existing approaches in the model-based robotics community can be highly effective when task geometry is known, but are complex and cumbersome to implement, and must be tailored to each individual problem by a qualified engineer. Within the learning community there is a long history of insertion research, but existing approaches are typically either too sample-inefficient to run on real robots, or assume access to high-level object features, e.g. socket pose. In this paper we show that relatively minor modifications to an off-the-shelf Deep-RL algorithm (DDPG), combined with a small number of human demonstrations, allows the robot to quickly learn to solve these tasks efficiently and robustly. Our approach requires no modeling or simulation, no parameterized search or alignment behaviors, no vision system aside from raw images, and no reward shaping. We evaluate our approach on a narrow-clearance peg-insertion task and a deformable clip-insertion task, both of which include variability in the socket position. Our results show that these tasks can be solved reliably on the real robot in less than 10 minutes of interaction time, and that the resulting policies are robust to variance in the socket position and orientation.

Connected through hyperedge HE-1 (induced by source paper "scaling data driven robotics with reward sketching and batch reinforcement learning"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "self supervised sim to real adaptation for visual robotic manipulation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras"):
  with members:
    - "pixel attentive policy gradient for multi fingered grasping in cluttered scenes"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "making sense of vision and touch learning multimodal representations for contact rich tasks"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "learning pose estimation for high precision robotic assembly using simulated depth images"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

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"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"

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"
    - "deep reinforcement learning for industrial insertion tasks with visual inputs and natural reward signals"
    - "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 "a framework for data driven robotics"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "challenges of real world reinforcement learning"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-6 (induced by source paper "end to end robotic reinforcement learning without reward engineering"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-7 (induced by source paper "positive unlabeled reward learning"):
  with members:
    - "reinforced imitation in heterogeneous action space"
    - "task relevant adversarial imitation learning"
    - "a framework for data driven robotics"
    - "deepmind control suite"
    - "classification from positive unlabeled and biased negative data"
    - "sample efficient imitation learning via generative adversarial nets"
    - "distributed prioritized experience replay"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"

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)