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: "gaze training by modulated dropout improves imitation learning"
  Abstract: Imitation learning by behavioral cloning is a prevalent method which has achieved some success in vision-based autonomous driving. The basic idea behind behavioral cloning is to have the neural network learn from observing a human expert's behavior. Typically, a convolutional neural network learns to predict the steering commands from raw driver-view images by mimicking the behaviors of human drivers. However, there are other cues, e.g. gaze behavior, available from human drivers that have yet to be exploited. Previous researches have shown that novice human learners can benefit from observing experts' gaze patterns. We present here that deep neural networks can also profit from this. We propose a method, gaze-modulated dropout, for integrating this gaze information into a deep driving network implicitly rather than as an additional input. Our experimental results demonstrate that gaze-modulated dropout enhances the generalization capability of the network to unseen scenes. Prediction error in steering commands is reduced by 23.5% compared to uniform dropout. Running closed loop in the simulator, the gaze-modulated dropout net increased the average distance travelled between infractions by 58.5%. Consistent with these results, we also found the gaze-modulated dropout net to have lower model uncertainty.

Connected through hyperedge HE-1 (induced by source paper "efficiently guiding imitation learning algorithms with human gaze"):
  with members:
    - "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"
    - "agil learning attention from human for visuomotor tasks"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "reward learning from human preferences and demonstrations in atari"

Connected through hyperedge HE-2 (induced by source paper "atari head atari human eye tracking and demonstration dataset"):
  with members:
    - "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"
    - "agil learning attention from human for visuomotor tasks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "hierarchical imitation and reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "robot navigation in crowds by graph convolutional networks with attention learned from human gaze"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "agil learning attention from human for visuomotor tasks"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "modeling cooperative navigation in dense human crowds"

Connected through hyperedge HE-4 (induced by source paper "utilizing eye gaze to enhance the generalization of imitation networks to unseen environments"):
  with members:
    - "end to end driving via conditional imitation learning"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "a deep multi level network for saliency prediction"
    - "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)