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: "goal oriented object importance estimation in on road driving videos"
  Abstract: We formulate a new problem as Object Importance Estimation (OIE) in on-road driving videos, where the road users are considered as important objects if they have influence on the control decision of the ego-vehicle's driver. The importance of a road user depends on both its visual dynamics, e.g., appearance, motion and location, in the driving scene and the driving goal, \emph{e.g}., the planned path, of the ego vehicle. We propose a novel framework that incorporates both visual model and goal representation to conduct OIE. To evaluate our framework, we collect an on-road driving dataset at traffic intersections in the real world and conduct human-labeled annotation of the important objects. Experimental results show that our goal-oriented method outperforms baselines and has much more improvement on the left-turn and right-turn scenarios. Furthermore, we explore the possibility of using object importance for driving control prediction and demonstrate that binary brake prediction can be improved with the information of object importance.

Connected through hyperedge HE-1 (induced by source paper "interaction graphs for object importance estimation in on road driving videos"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "graph r cnn for scene graph generation"
    - "videos as space time region graphs"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "predicting driver attention in critical situations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-2 (induced by source paper "who make drivers stop towards driver centric risk assessment risk object identification via causal inference"):
  with members:
    - "urban driving with conditional imitation learning"
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"
    - "causal confusion in imitation learning"
    - "grounding human to vehicle advice for self driving vehicles"
    - "causal induction from visual observations for goal directed tasks"
    - "a self validation network for object level human attention estimation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-3 (induced by source paper "egocentric vision based future vehicle localization for intelligent driving assistance systems"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"
    - "joint person segmentation and identification in synchronized first and third person videos"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "identifying first person camera wearers in third person videos"

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)