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 "review on computer vision techniques in emergency situations"):
  Abstract: In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.

Includes members:
  Member "abnormal event detection in videos using spatiotemporal autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "deep domain adaptation based video smoke detection using synthetic smoke images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "experimental exploration of compact convolutional neural network architectures for non temporal real time fire detection")
  Member "bandwidth limited object recognition in high resolution imagery":
    also connected through other hyperedges:
      (none)
  Member "anomaly detection in video using predictive convolutional long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "anomaly detection in road traffic using visual surveillance a survey")
  Member "fully convolutional neural network for fast anomaly detection in crowded scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "ten years of pedestrian detection what have we learned":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "virtual to real adaptation of pedestrian detectors for smart cities")
  Member "computer vision systems in road vehicles a review":
    also connected through other hyperedges:
      (none)

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)