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: "template matching with deformable diversity similarity"
  Abstract: We propose a novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a similarity measure, that is robust to complex deformations, significant background clutter, and occlusions. Empirical evaluation on the most up-to-date benchmark shows that our method outperforms the current state-of-the-art in its detection accuracy while improving computational complexity.

Connected through hyperedge HE-1 (induced by source paper "a critical analysis of self supervision or what we can learn from a single image"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "singan learning a generative model from a single natural image"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "cross pixel optical flow similarity for self supervised learning"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-2 (induced by source paper "surprising effectiveness of few image unsupervised feature learning"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised feature learning by learning to spot artifacts"
    - "transitive invariance for self supervised visual representation learning"
    - "improvements to context based self supervised learning"
    - "deep image prior"

Connected through hyperedge HE-3 (induced by source paper "comparison network for one shot conditional object detection"):
  with members:
    - "meta learning with latent embedding optimization"
    - "zero shot object detection by hybrid region embedding"
    - "metalearning with hebbian fast weights"
    - "large margin few shot learning"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "qatm quality aware template matching for deep learning"):
  with members:
    - "neighbourhood consensus networks"
    - "image to gps verification through a bottom up pattern matching network"
    - "heated up softmax embedding"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "deep matching and validation network an end to end solution to constrained image splicing localization and detection"
    - "learning spread out local feature descriptors"
    - "scnet learning semantic correspondence"

Connected through hyperedge HE-5 (induced by source paper "explaining away results in accurate and tolerant template matching"):
  with members:
    - "oatm occlusion aware template matching by consensus set maximization"
    - "improved descriptors for patch matching and reconstruction"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "lift learned invariant feature transform"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "best buddies similarity robust template matching using mutual nearest neighbors"
    - "learning to compare image patches via convolutional neural networks"
    - "domain size pooling in local descriptors dsp sift"

Connected through hyperedge HE-6 (induced by source paper "the contextual loss for image transformation with non aligned data"):
  with members:
    - "learning to maintain natural image statistics"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "style transfer via texture synthesis"

Connected through hyperedge HE-7 (induced by source paper "neural best buddies sparse cross domain correspondence"):
  with members:
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "nonlinear spectral image fusion"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "visual attribute transfer through deep image analogy"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "fast face swap using convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "image inpainting via generative multi column convolutional neural networks"):
  with members:
    - "the contextual loss for image transformation with non aligned data"
    - "shift net image inpainting via deep feature rearrangement"
    - "learning to maintain natural image statistics"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "high resolution image inpainting using multi scale neural patch synthesis"

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)