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: "combining self supervised learning and imitation for vision based rope manipulation"
  Abstract: Manipulation of deformable objects, such as ropes and cloth, is an important but challenging problem in robotics. We present a learning-based system where a robot takes as input a sequence of images of a human manipulating a rope from an initial to goal configuration, and outputs a sequence of actions that can reproduce the human demonstration, using only monocular images as input. To perform this task, the robot learns a pixel-level inverse dynamics model of rope manipulation directly from images in a self-supervised manner, using about 60K interactions with the rope collected autonomously by the robot. The human demonstration provides a high-level plan of what to do and the low-level inverse model is used to execute the plan. We show that by combining the high and low-level plans, the robot can successfully manipulate a rope into a variety of target shapes using only a sequence of human-provided images for direction.

Connected through hyperedge HE-1 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "contrastive multiview coding"
    - "learning robotic manipulation through visual planning and acting"
    - "learning to manipulate deformable objects without demonstrations"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-2 (induced by source paper "adversarial active exploration for inverse dynamics model learning"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "curious intrinsically motivated modular multi goal reinforcement learning"
    - "parameter space noise for exploration"
    - "hindsight experience replay"
    - "learning how to active learn a deep reinforcement learning approach"
    - "robust adversarial reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"

Connected through hyperedge HE-3 (induced by source paper "learning rope manipulation policies using dense object descriptors trained on synthetic depth data"):
  with members:
    - "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"
    - "learning robotic manipulation through visual planning and acting"
    - "grasp2vec learning object representations from self supervised grasping"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "learning compact geometric features"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "self imitation learning via trajectory conditioned policy for hard exploration tasks"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "behaviour suite for reinforcement learning"
    - "skew fit state covering self supervised reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "generative adversarial self imitation learning"
    - "exploration by random network distillation"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-5 (induced by source paper "learning to manipulate object collections using grounded state representations"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "deep set prediction networks"
    - "learning agile and dynamic motor skills for legged robots"
    - "relational deep reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-6 (induced by source paper "learning latent plans from play"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "behavioral cloning from observation"

Connected through hyperedge HE-7 (induced by source paper "towards a hypothesis on visual transformation based self supervision"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "self supervised dialogue learning"
    - "contrastive multiview coding"
    - "self supervised exploration via disagreement"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "the visual task adaptation benchmark"
    - "self supervised visual feature learning with deep neural networks a survey"

Connected through hyperedge HE-8 (induced by source paper "recent advances in imitation learning from observation"):
  with members:
    - "hybrid reinforcement learning with expert state sequences"
    - "sample efficient adversarial imitation learning from observation"
    - "imitation learning from video by leveraging proprioception"
    - "3d human pose machines with self supervised learning"
    - "ridm reinforced inverse dynamics modeling for learning from a single observed demonstration"
    - "imitating latent policies from observation"
    - "internal model from observations for reward shaping"
    - "an algorithmic perspective on imitation learning"

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.CV(Computer Vision and Pattern Recognition)