human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "vr goggles for robots real to sim domain adaptation for visual control"):
  Abstract: This paper deals with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks. Instead of adopting the common solutions to the problem by increasing the visual fidelity of synthetic images output from simulators during the training phase, this paper seeks to tackle the problem by translating the real-world image streams back to the synthetic domain during the deployment phase, to make the robot feel at home. We propose this as a lightweight, flexible, and efficient solution for visual control, as 1) no extra transfer steps are required during the expensive training of DRL agents in simulation; 2) the trained DRL agents will not be constrained to being deployable in only one specific real-world environment; 3) the policy training and the transfer operations are decoupled, and can be conducted in parallel. Besides this, we propose a conceptually simple yet very effective shift loss to constrain the consistency between subsequent frames, eliminating the need for optical flow. We validate the shift loss for artistic style transfer for videos and domain adaptation, and validate our visual control approach in real-world robot experiments. A video of our results is available at: this https URL

Includes members:
  Member "artistic style transfer for videos and spherical images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a content transformation block for image style transfer")
  Member "transfer learning from synthetic to real images using variational autoencoders for precise position detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity")
  Member "sem gan semantically consistent image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images")
  Member "curiosity driven exploration for mapless navigation with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "using simulation and domain adaptation to improve efficiency of deep robotic grasping":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "minos multimodal indoor simulator for navigation in complex environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "cycada cycle consistent adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a novel framework for selection of gans for an application")
  Member "carla an open urban driving simulator":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

Question: Please tell me which class the source paper 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)