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 review of robot learning for manipulation challenges representations and algorithms"
  Abstract: A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.

Connected through hyperedge HE-1 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer"):
  with members:
    - "6 dof grasping for target driven object manipulation in clutter"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

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"
    - "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"
    - "learning robotic assembly from cad"

Connected through hyperedge HE-3 (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"
    - "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"
    - "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-4 (induced by source paper "hypothesis driven skill discovery for hierarchical deep reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "monet unsupervised scene decomposition and representation"
    - "model based reinforcement learning for atari"
    - "multi object representation learning with iterative variational inference"
    - "exploration by random network distillation"
    - "learning real world robot policies by dreaming"
    - "relational deep reinforcement learning"
    - "probing physics knowledge using tools from developmental psychology"

Connected through hyperedge HE-5 (induced by source paper "experience embedded visual foresight"):
  with members:
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "videoflow a flow based generative model for video"
    - "hierarchical long term video prediction without supervision"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "zero shot visual imitation"
    - "modular meta learning"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"

Connected through hyperedge HE-6 (induced by source paper "self supervised correspondence in visuomotor policy learning"):
  with members:
    - "unsupervised learning of object keypoints for perception and control"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "graph structured visual imitation"
    - "unsupervised visuomotor control through distributional planning networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real reinforcement learning for deformable object manipulation"

Connected through hyperedge HE-7 (induced by source paper "better than demonstrator imitation learning via automatically ranked demonstrations"):
  with members:
    - "inverse reinforcement learning with multiple ranked experts"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "imitation learning from imperfect demonstration"
    - "risk aware active inverse reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "an algorithmic perspective on imitation learning"
    - "ensembledagger a bayesian approach to safe imitation learning"
    - "a survey of inverse reinforcement learning challenges methods and progress"

Connected through hyperedge HE-8 (induced by source paper "learning hybrid object kinematics for efficient hierarchical planning under uncertainty"):
  with members:
    - "category level articulated object pose estimation"
    - "unsupervised discovery of parts structure and dynamics"
    - "inferring geometric constraints in human demonstrations"
    - "efficient hierarchical robot motion planning under uncertainty and hybrid dynamics"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "learning articulated motions from visual demonstration"

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)