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 "context aware road user importance estimation icare"):
  Abstract: Road-users are a critical part of decision-making for both self-driving cars and driver assistance systems. Some road-users, however, are more important for decision-making than others because of their respective intentions, ego vehicle's intention and their effects on each other. In this paper, we propose a novel architecture for road-user importance estimation which takes advantage of the local and global context of the scene. For local context, the model exploits the appearance of the road users (which captures orientation, intention, etc.) and their location relative to ego-vehicle. The global context in our model is defined based on the feature map of the convolutional layer of the module which predicts the future path of the ego-vehicle and contains rich global information of the scene (e.g., infrastructure, road lanes, etc.), as well as the ego vehicle's intention information. Moreover, this paper introduces a new data set of real-world driving, concentrated around inter-sections and includes annotations of important road users. Systematic evaluations of our proposed method against several baselines show promising results.

Includes members:
  Member "predicting human eye fixations via an lstm based saliency attentive model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how much position information do convolutional neural networks encode")
  Member "computer vision for autonomous vehicles problems datasets and state of the art":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "person re identification using visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse label smoothing regularization for person re identification")
  Member "predicting the driver s focus of attention the dr eye ve project":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interaction graphs for object importance estimation in on road driving videos")
  Member "end to end learning for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "deepsaliency multi task deep neural network model for salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "recurrent attentional networks for saliency detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")

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)