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 "eyecar modeling the visual attention allocation of drivers in semi autonomous vehicles"):
  Abstract: A safe transition between autonomous and manual control requires sustained visual attention of the driver for the perception and assessment of hazards in dynamic driving environments. Thus, drivers must retain a certain level of situation awareness to safely takeover. Understanding the visual attention allocation of drivers can pave the way for inferring their dynamic state of situational awareness. We propose a reinforcement and inverse-reinforcement learning framework for modeling passive drivers' visual attention allocation in semi-autonomous vehicles. The proposed approach measures the eye-movement of passive drivers to evaluate their responses to real-world rear-end collisions. The results show substantial individual differences in the eye fixation patterns by driving experience, even among fully attentive drivers. Experienced drivers were more attentive to the situational dynamics and were able to identify potentially hazardous objects before any collisions occurred. These models of visual attention could potentially be integrated into autonomous systems to continuously monitor and guide effective intervention. #R##N#Keywords: Visual attention allocation; Situation awareness; Eye movements; Eye fixation; Eye-Tracking; Reinforcement Learning; Inverse Reinforcement Learning

Includes members:
  Member "a deep reinforcement learning based driving policy for autonomous road vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "integrating deep reinforcement learning with model based path planners for automated driving")
  Member "driving with style inverse reinforcement learning in general purpose planning for automated driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "driving style encoder situational reward adaptation for general purpose planning in automated driving")
  Member "agil learning attention from human for visuomotor tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "the effect of whole body haptic feedback on driver s perception in negotiating a curve":
    also connected through other hyperedges:
      (none)
  Member "joint attention in driver pedestrian interaction from theory to practice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "looking to relations for future trajectory forecast")
  Member "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "predicting driver attention in critical situations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interaction graphs for object importance estimation in on road driving videos")
  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)