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 "small target detection for search and rescue operations using distributed deep learning and synthetic data generation"):
  Abstract: It is important to find the target as soon as possible for search and rescue operations. Surveillance camera systems and unmanned aerial vehicles (UAVs) are used to support search and rescue. Automatic object detection is important because a person cannot monitor multiple surveillance screens simultaneously for 24 hours. Also, the object is often too small to be recognized by the human eye on the surveillance screen. This study used UAVs around the Port of Houston and fixed surveillance cameras to build an automatic target detection system that supports the US Coast Guard (USCG) to help find targets (e.g., person overboard). We combined image segmentation, enhancement, and convolution neural networks to reduce detection time to detect small targets. We compared the performance between the auto-detection system and the human eye. Our system detected the target within 8 seconds, but the human eye detected the target within 25 seconds. Our systems also used synthetic data generation and data augmentation techniques to improve target detection accuracy. This solution may help the search and rescue operations of the first responders in a timely manner.

Includes members:
  Member "predicting rapid fire growth flashover using conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks for failure prediction")
  Member "occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep neural networks for pattern recognition")
  Member "automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model":
    also connected through other hyperedges:
      (none)
  Member "feature fused ssd fast detection for small objects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dc spp yolo dense connection and spatial pyramid pooling based yolo for object detection")
  Member "deep neural networks for pattern recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detection and identification of objects and humans in thermal images")
  Member "procedural modeling and physically based rendering for synthetic data generation in automotive applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data")
  Member "detecting small signs from large images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient object detection in large images using deep reinforcement learning")
  Member "unrealcv connecting computer vision to unreal engine":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation")

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)