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: "unsupervised traffic accident detection in first person videos"
  Abstract: Recognizing abnormal events such as traffic violations and accidents in natural driving scenes is essential for successful autonomous driving and advanced driver assistance systems. However, most work on video anomaly detection suffers from two crucial drawbacks. First, they assume cameras are fixed and videos have static backgrounds, which is reasonable for surveillance applications but not for vehicle-mounted cameras. Second, they pose the problem as one-class classification, relying on arduously hand-labeled training datasets that limit recognition to anomaly categories that have been explicitly trained. This paper proposes an unsupervised approach for traffic accident detection in first-person (dashboard-mounted camera) videos. Our major novelty is to detect anomalies by predicting the future locations of traffic participants and then monitoring the prediction accuracy and consistency metrics with three different strategies. We evaluate our approach using a new dataset of diverse traffic accidents, AnAn Accident Detection (A3D), as well as another publicly-available dataset. Experimental results show that our approach outperforms the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "lates latent space distillation for teacher student driving policy learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "yolov3 an incremental improvement"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "latent space autoregression for novelty detection"

Connected through hyperedge HE-2 (induced by source paper "dada 2000 can driving accident be predicted by driver attention analyzed by a benchmark"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "is it safe to drive an overview of factors challenges and datasets for driveability assessment in autonomous driving"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "drive video analysis for the detection of traffic near miss incidents"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"

Connected through hyperedge HE-3 (induced by source paper "temporal recurrent networks for online action detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "fully coupled two stream spatiotemporal networks for extremely low resolution action recognition"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "predictive corrective networks for action detection"

Connected through hyperedge HE-4 (induced by source paper "egoreid dataset person re identification in videos acquired by mobile devices with first person point of view"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "person search with natural language description"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"

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)