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: "affordance learning for end to end visuomotor robot control"
  Abstract: Training end-to-end deep robot policies requires a lot of domain-, task-, and hardware-specific data, which is often costly to provide. In this work, we propose to tackle this issue by employing a deep neural network with a modular architecture, consisting of separate perception, policy, and trajectory parts. Each part of the system is trained fully on synthetic data or in simulation. The data is exchanged between parts of the system as low-dimensional latent representations of affordances and trajectories. The performance is then evaluated in a zero-shot transfer scenario using Franka Panda robot arm. Results demonstrate that a low-dimensional representation of scene affordances extracted from an RGB image is sufficient to successfully train manipulator policies. We also introduce a method for affordance dataset generation, which is easily generalizable to new tasks, objects and environments, and requires no manual pixel labeling.

Connected through hyperedge HE-1 (induced by source paper "invisible marker automatic annotation of segmentation masks for object manipulation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "fluid annotation a human machine collaboration interface for full image annotation"
    - "interactively picking real world objects with unconstrained spoken language instructions"
    - "recent trends in deep learning based natural language processing"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "detection and tracking of liquids with fully convolutional networks"
    - "coco stuff thing and stuff classes in context"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "adversarial feature training for generalizable robotic visuomotor control"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "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"
    - "deep object centric representations for generalizable robot learning"
    - "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-3 (induced by source paper "meta reinforcement learning for sim to real domain adaptation"):
  with members:
    - "feedback based fabric strip folding"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "how to train your maml"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

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)