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: "anticipating traffic accidents with adaptive loss and large scale incident db"
  Abstract: In this paper, we propose a novel approach for traffic accident anticipation through (i) Adaptive Loss for Early Anticipation (AdaLEA) and (ii) a large-scale self-annotated incident database for anticipation. The proposed AdaLEA allows a model to gradually learn an earlier anticipation as training progresses. The loss function adaptively assigns penalty weights depending on how early the model can an- ticipate a traffic accident at each epoch. Additionally, we construct a Near-miss Incident DataBase for anticipation. This database contains an enormous number of traffic near- miss incident videos and annotations for detail evaluation of two tasks, risk anticipation and risk-factor anticipation. In our experimental results, we found our proposal achieved the highest scores for risk anticipation (+6.6% better on mean average precision (mAP) and 2.36 sec earlier than previous work on the average time-to-collision (ATTC)) and risk-factor anticipation (+4.3% better on mAP and 0.70 sec earlier than previous work on ATTC).

Connected through hyperedge HE-1 (induced by source paper "intelligent intersection two stream convolutional networks for real time near accident detection in traffic video"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "deep cosine metric learning for person re identification"
    - "drive video analysis for the detection of traffic near miss incidents"
    - "cadp a novel dataset for cctv traffic camera based accident analysis"
    - "in defense of the triplet loss for person re identification"
    - "single shot text detector with regional attention"
    - "simple online and realtime tracking with a deep association metric"
    - "simple online and realtime tracking"

Connected through hyperedge HE-2 (induced by source paper "forecasting time to collision from monocular video feasibility dataset and challenges"):
  with members:
    - "a baseline for 3d multi object tracking"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "learning spatio temporal features with two stream deep 3d cnns for lipreading"
    - "a visual neural network for robust collision perception in vehicle driving scenarios"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "yolov3 an incremental improvement"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-3 (induced by source paper "dada 2000 can driving accident be predicted by driver attention analyzed by a benchmark"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "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"
    - "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-4 (induced by source paper "detection of collision prone vehicle behavior at intersections using siamese interaction lstm"):
  with members:
    - "vision meets drones a challenge"
    - "real world anomaly detection in surveillance videos"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "social attention modeling attention in human crowds"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "failure prediction for autonomous driving"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "learning driving models with a surround view camera system and a route planner"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "predicting deeper into the future of semantic segmentation"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "brain inspired cognitive model with attention for self driving cars"
    - "the cityscapes dataset for semantic urban scene understanding"

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)