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: "agent centric risk assessment accident anticipation and risky region localization"
  Abstract: For survival, a living agent must have the ability to assess risk (1) by temporally anticipating accidents before they occur, and (2) by spatially localizing risky regions in the environment to move away from threats. In this paper, we take an agent-centric approach to study the accident anticipation and risky region localization tasks. We propose a novel soft-attention Recurrent Neural Network (RNN) which explicitly models both spatial and appearance-wise non-linear interaction between the agent triggering the event and another agent or static-region involved. In order to test our proposed method, we introduce the Epic Fail (EF) dataset consisting of 3000 viral videos capturing various accidents. In the experiments, we evaluate the risk assessment accuracy both in the temporal domain (accident anticipation) and spatial domain (risky region localization) on our EF dataset and the Street Accident (SA) dataset. Our method consistently outperforms other baselines on both datasets.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "is the pedestrian going to cross answering by 2d pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "scene graph generation via conditional random fields"

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"
    - "goal oriented object importance estimation in on road driving videos"
    - "causal induction from visual observations for goal directed tasks"
    - "a self validation network for object level human attention estimation"

Connected through hyperedge HE-3 (induced by source paper "forecasting future sequence of actions to complete an activity"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "forecasting future action sequences with neural memory networks"
    - "action anticipation by predicting future dynamic images"
    - "when will you do what anticipating temporal occurrences of activities"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "predicting human activities using stochastic grammar"
    - "encouraging lstms to anticipate actions very early"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-4 (induced by source paper "anticipating traffic accidents with adaptive loss and large scale incident db"):
  with members:
    - "drive video analysis for the detection of traffic near miss incidents"
    - "encouraging lstms to anticipate actions very early"
    - "quasi recurrent neural networks"
    - "torontocity seeing the world with a million eyes"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "anticipating visual representations from unlabeled video"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "visual forecasting by imitating dynamics in natural sequences"):
  with members:
    - "adversarial learning for neural dialogue generation"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning visual storylines with skipping recurrent neural networks"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "generative adversarial imitation learning"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "learning features by watching objects move"
    - "model free imitation learning with policy optimization"

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)