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: "agil learning attention from human for visuomotor tasks"
  Abstract: When intelligent agents learn visuomotor behaviors from human demonstrations, they may benefit from knowing where the human is allocating visual attention, which can be inferred from their gaze. A wealth of information regarding intelligent decision making is conveyed by human gaze allocation; hence, exploiting such information has the potential to improve the agents' performance. With this motivation, we propose the AGIL (Attention Guided Imitation Learning) framework. We collect high-quality human action and gaze data while playing Atari games in a carefully controlled experimental setting. Using these data, we first train a deep neural network that can predict human gaze positions and visual attention with high accuracy (the gaze network) and then train another network to predict human actions (the policy network). Incorporating the learned attention model from the gaze network into the policy network significantly improves the action prediction accuracy and task performance.

Connected through hyperedge HE-1 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable reinforcement learning through a causal lens"
    - "atari head atari human eye tracking and demonstration dataset"
    - "conservative q improvement reinforcement learning for an interpretable decision tree policy"
    - "free lunch saliency via attention in atari agents"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "towards better interpretability in deep q networks"

Connected through hyperedge HE-2 (induced by source paper "efficiently guiding imitation learning algorithms with human gaze"):
  with members:
    - "gaze training by modulated dropout improves imitation learning"
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "understanding teacher gaze patterns for robot learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "inverse reinforcement learning for video games"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "reward learning from human preferences and demonstrations in atari"

Connected through hyperedge HE-3 (induced by source paper "free lunch saliency via attention in atari agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "atari head atari human eye tracking and demonstration dataset"
    - "visual rationalizations in deep reinforcement learning for atari games"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "an introduction to deep reinforcement learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"

Connected through hyperedge HE-4 (induced by source paper "leveraging human guidance for deep reinforcement learning tasks"):
  with members:
    - "imitation learning from video by leveraging proprioception"
    - "atari head atari human eye tracking and demonstration dataset"
    - "recent advances in imitation learning from observation"
    - "imitating latent policies from observation"
    - "an algorithmic perspective on imitation learning"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "learning actionable representations from visual observations"
    - "generative adversarial imitation from observation"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning with attention that works a self supervised approach"):
  with members:
    - "visual rationalizations in deep reinforcement learning for atari games"
    - "an initial attempt of combining visual selective attention with deep reinforcement learning"
    - "distributed prioritized experience replay"
    - "attention aware generative adversarial networks ata gans"
    - "explainable and explicit visual reasoning over scene graphs"
    - "non local neural networks"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "atari head atari human eye tracking and demonstration dataset"):
  with members:
    - "gaze training by modulated dropout improves imitation learning"
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "minerl a large scale dataset of minecraft demonstrations"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "free lunch saliency via attention in atari agents"
    - "pre training with non expert human demonstration for deep reinforcement learning"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "hierarchical imitation and reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "human visual attention prediction boosts learning performance of autonomous driving agents"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "gaze based context aware robotic system for assisted reaching and grasping"
    - "on offline evaluation of vision based driving models"
    - "attention is all you need"
    - "carla an open urban driving simulator"
    - "end to end driving via conditional imitation learning"
    - "predicting the driver s focus of attention the dr eye ve project"

Connected through hyperedge HE-8 (induced by source paper "gaze training by modulated dropout improves imitation learning"):
  with members:
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "real time workload classification during driving using hypernetworks"
    - "virtual to real reinforcement learning for autonomous driving"
    - "carla an open urban driving simulator"
    - "end to end driving via conditional imitation learning"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "end to end learning for self driving cars"

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)