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 "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.

Includes members:
  Member "agil learning attention from human for visuomotor tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "cirl controllable imitative reinforcement learning for vision based self driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multimodal end to end autonomous driving")
  Member "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "real time workload classification during driving using hypernetworks":
    also connected through other hyperedges:
      (none)
  Member "virtual to real reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "carla an open urban driving simulator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "end to end driving via conditional imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "predicting the driver s focus of attention the dr eye ve project":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interaction graphs for object importance estimation in on road driving videos")

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.CV(Computer Vision and Pattern Recognition)