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: "infrared colorization using deep convolutional neural networks"
  Abstract: This paper proposes a method for transferring the RGB color spectrum to near-infrared (NIR) images using deep multi-scale convolutional neural networks. A direct and integrated transfer between NIR and RGB pixels is trained. The trained model does not require any user guidance or a reference image database in the recall phase to produce images with a natural appearance. To preserve the rich details of the NIR image, its high frequency features are transferred to the estimated RGB image. The presented approach is trained and evaluated on a real-world dataset containing a large amount of road scene images in summer. The dataset was captured by a multi-CCD NIR/RGB camera, which ensures a perfect pixel to pixel registration.

Connected through hyperedge HE-1 (induced by source paper "robust perceptual night vision in thermal colorization"):
  with members:
    - "multimodal sensor fusion in single thermal image super resolution"
    - "pixcolor pixel recursive colorization"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "learning representations for automatic colorization"
    - "image to image translation with conditional adversarial networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation"):
  with members:
    - "semantic nighttime image segmentation with synthetic stylized data gradual adaptation and uncertainty aware evaluation"
    - "learning to adapt structured output space for semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "map management for efficient long term visual localization in outdoor environments"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "night to day image translation for retrieval based localization"

Connected through hyperedge HE-3 (induced by source paper "unpaired thermal to visible spectrum transfer using adversarial training"):
  with members:
    - "colorization as a proxy task for visual understanding"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "toward multimodal image to image translation"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"
    - "multispectral deep neural networks for pedestrian detection"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal channel correlation networks for action classification"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "look listen and learn"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "squeeze and excitation networks"
    - "convnet architecture search for spatiotemporal feature learning"

Connected through hyperedge HE-5 (induced by source paper "learning color space adaptation from synthetic to real images of cirrus clouds"):
  with members:
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "cycada cycle consistent adversarial domain adaptation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "mask r cnn"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "rendergan generating realistic labeled data"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-6 (induced by source paper "pixel level semantics guided image colorization"):
  with members:
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "probabilistic image colorization"
    - "outline colorization through tandem adversarial networks"
    - "learning representations for automatic colorization"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

Connected through hyperedge HE-7 (induced by source paper "ir2vi enhanced night environmental perception by unsupervised thermal image translation"):
  with members:
    - "multi channel cnn based object detection for enhanced situation awareness"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "photographic image synthesis with cascaded refinement networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "benchmark dataset for automatic damaged building detection from post hurricane remotely sensed imagery"):
  with members:
    - "xview objects in context in overhead imagery"
    - "detecting damaged buildings on post hurricane satellite imagery based on customized convolutional neural networks"
    - "building damage annotation on post hurricane satellite imagery based on convolutional neural networks"
    - "focal loss for dense object detection"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "faster r cnn towards real time object detection with region proposal networks"

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)