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: "multimodal sensor fusion in single thermal image super resolution"
  Abstract: With the fast growth in the visual surveillance and security sectors, thermal infrared images have become increasingly necessary ina large variety of industrial applications. This is true even though IR sensors are still more expensive than their RGB counterpart having the same resolution. In this paper, we propose a deep learning solution to enhance the thermal image resolution. The following results are given:(I) Introduction of a multimodal, visual-thermal fusion model that ad-dresses thermal image super-resolution, via integrating high-frequency information from the visual image. (II) Investigation of different net-work architecture schemes in the literature, their up-sampling methods,learning procedures, and their optimization functions by showing their beneficial contribution to the super-resolution problem. (III) A bench-mark ULB17-VT dataset that contains thermal images and their visual images counterpart is presented. (IV) Presentation of a qualitative evaluation of a large test set with 58 samples and 22 raters which shows that our proposed model performs better against state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "pyramidal edge maps based guided thermal super resolution"):
  with members:
    - "keyframe based direct thermal inertial odometry"
    - "multimedia super resolution via deep learning a survey"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "richer convolutional features for edge detection"
    - "fully convolutional siamese networks for object tracking"
    - "densely connected convolutional networks"
    - "a deep primal dual network for guided depth super resolution"

Connected through hyperedge HE-2 (induced by source paper "robust perceptual night vision in thermal colorization"):
  with members:
    - "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"
    - "infrared colorization using deep convolutional neural networks"
    - "image to image translation with conditional adversarial networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "pst900 rgb thermal calibration dataset and segmentation network"):
  with members:
    - "fast scnn fast semantic segmentation network"
    - "mavnet an effective semantic segmentation micro network for mav based tasks"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "understanding convolution for semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "you only look once unified real time object detection"

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.LG(Machine Learning)