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 "who make drivers stop towards driver centric risk assessment risk object identification via causal inference"):
  Abstract: We propose a framework based on causal inference for risk object identification, an essential task towards driver-centric risk assessment. In this work, risk objects are defined as objects influencing driver's goal-oriented behavior. There are two limitations of the existing approaches. First, they require strong supervisions such as risk object location or human gaze location. Second, there is no explicit reasoning stage for identifying risk object. To address these issues, the task of identifying causes of driver behavioral change is formalized in the language of functional causal models and interventions. Specifically, we iteratively simulate causal effect by removing an object using the proposed driving model. The risk object is determined as the one causing the most substantial causal effect. We evaluate the proposed framework on the Honda Research Institute Driving Dataset (HDD). The dataset provides the annotation for risk object localization to enable systematic benchmarking with existing approaches. Our framework demonstrates a substantial average performance boost over a strong baseline by 7.5%.

Includes members:
  Member "urban driving with conditional imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "exploring the limitations of behavior cloning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies")
  Member "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks":
    also connected through other hyperedges:
      (none)
  Member "causal confusion in imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "grounding human to vehicle advice for self driving vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "talk2nav long range vision and language navigation with dual attention and spatial memory")
  Member "goal oriented object importance estimation in on road driving videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interaction graphs for object importance estimation in on road driving videos")
  Member "causal induction from visual observations for goal directed tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unbiased scene graph generation from biased training")
  Member "a self validation network for object level human attention estimation":
    also connected through other hyperedges:
      (none)

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)