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: "deep domain adaptation based video smoke detection using synthetic smoke images"
  Abstract: In this paper, a deep domain adaptation based method for video smoke detection is proposed to extract a powerful feature representation of smoke. Due to the smoke image samples limited in scale and diversity for deep CNN training, we systematically produced adequate synthetic smoke images with a wide variation in the smoke shape, background and lighting conditions. Considering that the appearance gap (dataset bias) between synthetic and real smoke images degrades significantly the performance of the trained model on the test set composed fully of real images, we build deep architectures based on domain adaptation to confuse the distributions of features extracted from synthetic and real smoke images. This approach expands the domain-invariant feature space for smoke image samples. With their approximate feature distribution separated from non-smoke images, the recognition rate of the trained model is improved significantly compared with the model trained directly on mixed dataset of synthetic and real images. Experimentally, several deep architectures with different design choices are applied to the smoke detector. The ultimate framework can get a satisfactory result on the test set. We believe that our method own strong robustness and may offer a new way for the video smoke detection.

Connected through hyperedge HE-1 (induced by source paper "experimental exploration of compact convolutional neural network architectures for non temporal real time fire detection"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-2 (induced by source paper "video smoke detection based on deep saliency network"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "rgbd salient object detection via deep fusion"
    - "what can help pedestrian detection"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"

Connected through hyperedge HE-3 (induced by source paper "review on computer vision techniques in emergency situations"):
  with members:
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "bandwidth limited object recognition in high resolution imagery"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "fully convolutional neural network for fast anomaly detection in crowded scenes"
    - "ten years of pedestrian detection what have we learned"
    - "computer vision systems in road vehicles a review"

Connected through hyperedge HE-4 (induced by source paper "domain adaptation from synthesis to reality in single model detector for video smoke detection"):
  with members:
    - "unsupervised deep domain adaptation for pedestrian detection"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "focal loss for dense object detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "feature pyramid networks for object detection"
    - "deep coral correlation alignment for deep domain adaptation"

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)