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: "transfer learning for related reinforcement learning tasks via image to image translation"
  Abstract: Despite the remarkable success of Deep RL in learning control policies from raw pixels, the resulting models do not generalize. We demonstrate that a trained agent fails completely when facing small visual changes, and that fine-tuning---the common transfer learning paradigm---fails to adapt to these changes, to the extent that it is faster to re-train the model from scratch. We show that by separating the visual transfer task from the control policy we achieve substantially better sample efficiency and transfer behavior, allowing an agent trained on the source task to transfer well to the target tasks. The visual mapping from the target to the source domain is performed using unaligned GANs, resulting in a control policy that can be further improved using imitation learning from imperfect demonstrations. We demonstrate the approach on synthetic visual variants of the Breakout game, as well as on transfer between subsequent levels of Road Fighter, a Nintendo car-driving game. A visualization of our approach can be seen in this https URL and this https URL .

Connected through hyperedge HE-1 (induced by source paper "provably efficient third person imitation from offline observation"):
  with members:
    - "cross domain imitation learning"
    - "generative adversarial imitation from observation"
    - "behavioral cloning from observation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "third person imitation learning"
    - "one shot imitation learning"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-2 (induced by source paper "network randomization a simple technique for generalization in deep reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "environment probing interaction policies"
    - "domain randomization for active pose estimation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "playing hard exploration games by watching youtube"
    - "learning not to learn training deep neural networks with biased data"

Connected through hyperedge HE-3 (induced by source paper "robust domain randomization for reinforcement learning"):
  with members:
    - "active domain randomization"
    - "unsupervised data augmentation for consistency training"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "generalization and regularization in dqn"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "continuous control with contexts provably"):
  with members:
    - "extra transfer guided exploration"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "contextual markov decision processes using generalized linear models"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "learning and planning with a semantic model"
    - "sample efficient reinforcement learning through transfer and architectural priors"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-5 (induced by source paper "vrls a unified reinforcement learning scheduler for vehicle to vehicle communications"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "radio resource allocation for reliable out of coverage v2v communications"
    - "reinforcement learning scheduler for vehicle to vehicle communications outside coverage"
    - "asynchronous methods for deep reinforcement learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "visual analogies between atari games for studying transfer learning in rl"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "progressive neural networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "improved training of wasserstein gans"
    - "one sided unsupervised domain mapping"
    - "attribute guided face generation using conditional cyclegan"
    - "asynchronous methods for deep reinforcement learning"
    - "playing atari with deep reinforcement 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)