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 "periphery fovea multi resolution driving model guided by human attention"):
  Abstract: Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the model processes the full video frames in low resolution. Its foveal vision module selects sub-regions and uses high-resolution input from those regions to improve its driving performance. We train the fovea selection module with supervision from driver gaze. We show that adding high-resolution input from predicted human driver gaze locations significantly improves the driving accuracy of the model. Our periphery-fovea multi-resolution model outperforms a uni-resolution periphery-only model that has the same amount of floating-point operations. More importantly, we demonstrate that our driving model achieves a significantly higher performance gain in pedestrian-involved critical situations than in other non-critical situations.

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 "textual explanations for self driving vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining with counter visual attributes and examples")
  Member "deep object centric policies for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "interpretable learning for self driving cars by visualizing causal attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable end to end urban autonomous driving with latent deep reinforcement learning")
  Member "predicting driver attention in critical situations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interaction graphs for object importance estimation in on road driving videos")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  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")
  Member "learning where to attend like a human driver":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "see attend and brake an attention based saliency map prediction model for end to end driving")

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)