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: "drive video analysis for the detection of traffic near miss incidents"
  Abstract: Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).

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"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "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 "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"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "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 "adversarial perturbations against real time video classification systems"):
  with members:
    - "nag network for adversary generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stacked generative adversarial networks"
    - "universal adversarial perturbations"

Connected through hyperedge HE-4 (induced by source paper "anticipating traffic accidents with adaptive loss and large scale incident db"):
  with members:
    - "agent centric risk assessment accident anticipation and risky region localization"
    - "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"

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)