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 "robust perceptual night vision in thermal colorization"):
  Abstract: Transforming a thermal infrared image into a robust perceptual colour Visible image is an ill-posed problem due to the differences in their spectral domains and in the objects' representations. Objects appear in one spectrum but not necessarily in the other, and the thermal signature of a single object may have different colours in its Visible representation. This makes a direct mapping from thermal to Visible images impossible and necessitates a solution that preserves texture captured in the thermal spectrum while predicting the possible colour for certain objects. In this work, a deep learning method to map the thermal signature from the thermal image's spectrum to a Visible representation in their low-frequency space is proposed. A pan-sharpening method is then used to merge the predicted low-frequency representation with the high-frequency representation extracted from the thermal image. The proposed model generates colour values consistent with the Visible ground truth when the object does not vary much in its appearance and generates averaged grey values in other cases. The proposed method shows robust perceptual night vision images in preserving the object's appearance and image context compared with the existing state-of-the-art.

Includes members:
  Member "multimodal sensor fusion in single thermal image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pyramidal edge maps based guided thermal super resolution")
  Member "pixcolor pixel recursive colorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guided image generation with conditional invertible neural networks")
  Member "tv gan generative adversarial network based thermal to visible face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relational deep feature learning for heterogeneous face recognition")
  Member "unsupervised diverse colorization via generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chromagan adversarial picture colorization with semantic class distribution")
  Member "learning representations for automatic colorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "infrared colorization using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t worry about the weather unsupervised condition dependent domain adaptation")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "delving deep into rectifiers surpassing human level performance on imagenet classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)