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: "analysing domain shift factors between videos and images for object detection"
  Abstract: Object detection is one of the most important challenges in computer vision. Object detectors are usually trained on bounding-boxes from still images. Recently, video has been used as an alternative source of data. Yet, for a given test domain (image or video), the performance of the detector depends on the domain it was trained on. In this paper, we examine the reasons behind this performance gap. We define and evaluate different domain shift factors: spatial location accuracy, appearance diversity, image quality and aspect distribution. We examine the impact of these factors by comparing performance before and after factoring them out. The results show that all four factors affect the performance of the detectors and their combined effect explains nearly the whole performance gap.

Connected through hyperedge HE-1 (induced by source paper "reveal of domain effect how visual restoration contributes to object detection in aquatic scenes"):
  with members:
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "a robust learning approach to domain adaptive object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "selective refinement network for high performance face detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of foreground object segmentation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "unsupervised representation learning by sorting sequences"
    - "segflow joint learning for video object segmentation and optical flow"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-3 (induced by source paper "automatic adaptation of object detectors to new domains using self training"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "unsupervised hard example mining from videos for improved object detection"
    - "theory of curriculum learning with convex loss functions"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "domain adaptation for visual applications a comprehensive survey"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "spacetime graph optimization for video object segmentation"):
  with members:
    - "unsupervised online video object segmentation with motion property understanding"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "video object segmentation without temporal information"

Connected through hyperedge HE-5 (induced by source paper "pnp adanet plug and play adversarial domain adaptation network with a benchmark at cross modality cardiac segmentation"):
  with members:
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "generalization bounds for unsupervised cross domain mapping with wgans"
    - "domain and geometry agnostic cnns for left atrium segmentation in 3d ultrasound"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised adversarial visual level domain adaptation for learning video object detectors from images"):
  with members:
    - "receptive field block net for accurate and fast object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "image to image translation with conditional adversarial networks"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "training object class detectors with click supervision"):
  with members:
    - "transfer learning by ranking for weakly supervised object annotation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "click carving segmenting objects in video with point clicks"
    - "spot on action localization from pointly supervised proposals"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "embracing error to enable rapid crowdsourcing"
    - "contextlocnet context aware deep network models for weakly supervised localization"

Connected through hyperedge HE-8 (induced by source paper "extreme clicking for efficient object annotation"):
  with members:
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "click carving segmenting objects in video with point clicks"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "weakly supervised deep detection networks"
    - "fast r cnn"
    - "discovering attribute shades of meaning with the crowd"

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)