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 object centric representations for generalizable robot learning"
  Abstract: Robotic manipulation in complex open-world scenarios requires both reliable physical manipulation skills and effective and generalizable perception. In this paper, we propose a method where general purpose pretrained visual models serve as an object-centric prior for the perception system of a learned policy. We devise an object-level attentional mechanism that can be used to determine relevant objects from a few trajectories or demonstrations, and then immediately incorporate those objects into a learned policy. A task-independent meta-attention locates possible objects in the scene, and a task-specific attention identifies which objects are predictive of the trajectories. The scope of the task-specific attention is easily adjusted by showing demonstrations with distractor objects or with diverse relevant objects. Our results indicate that this approach exhibits good generalization across object instances using very few samples, and can be used to learn a variety of manipulation tasks using reinforcement learning.

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"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "mapping state space using landmarks for universal goal reaching"
    - "object oriented dynamics predictor"
    - "inverse reinforcement learning for video games"
    - "grasp2vec learning object representations from self supervised grasping"
    - "count based exploration with the successor representation"

Connected through hyperedge HE-3 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "instance and category level 6d object pose estimation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "bop benchmark for 6d object pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"

Connected through hyperedge HE-4 (induced by source paper "graph structured visual imitation"):
  with members:
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"
    - "learning actionable representations from visual observations"
    - "zero shot visual imitation"
    - "graph networks as learnable physics engines for inference and control"
    - "state representation learning for control an overview"
    - "sfv reinforcement learning of physical skills from videos"
    - "relational inductive biases deep learning and graph networks"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-5 (induced by source paper "plan arithmetic compositional plan vectors for multi task control"):
  with members:
    - "hierarchical visuomotor control of humanoids"
    - "composable deep reinforcement learning for robotic manipulation"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "latent space policies for hierarchical reinforcement learning"
    - "task embedded control networks for few shot imitation learning"
    - "neural probabilistic motor primitives for humanoid control"
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"

Connected through hyperedge HE-6 (induced by source paper "monocular plan view networks for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "conditional affordance learning for driving in urban environments"
    - "driving policy transfer via modularity and abstraction"
    - "deep object centric policies for autonomous driving"
    - "on offline evaluation of vision based driving models"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "explaining how a deep neural network trained with end to end learning steers a car"

Connected through hyperedge HE-7 (induced by source paper "adversarial feature training for generalizable robotic visuomotor control"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "affordance learning for end to end visuomotor robot control"
    - "beyond sharing weights for deep domain adaptation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep reinforcement learning to acquire navigation skills for wheel legged robots in complex environments"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "accept synthetic objects as real end to end training of attentive deep visuomotor policies for manipulation in clutter"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "pyrobot an open source robotics framework for research and benchmarking"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "craves controlling robotic arm with a vision based economic system"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "video fill in the blank using lr rl lstms with spatial temporal attentions"

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)