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: "neural best buddies sparse cross domain correspondence"
  Abstract: A deep learning based method for sparse correspondence between pairs of objects that belong to different semantic categories and may differ drastically in their appearance, but contain semantically related parts.

Connected through hyperedge HE-1 (induced by source paper "cross domain cascaded deep feature translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "logan unpaired shape transform in latent overcomplete space"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "improving shape deformation in unsupervised image to image translation"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-2 (induced by source paper "semantic attribute matching networks"):
  with members:
    - "recurrent transformer networks for semantic correspondence"
    - "a closed form solution to photorealistic image stylization"
    - "neighbourhood consensus networks"
    - "arbitrary style transfer with deep feature reshuffle"
    - "convolutional neural network architecture for geometric matching"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"

Connected through hyperedge HE-3 (induced by source paper "cross domain image manipulation by demonstration"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "training deep face recognition systems with synthetic data"
    - "multimodal unsupervised image to image translation"
    - "dida disentangled synthesis for domain adaptation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"

Connected through hyperedge HE-4 (induced by source paper "sketchzooms deep multi view descriptors for matching line drawings"):
  with members:
    - "triplet center loss for multi view 3d object retrieval"
    - "sampling matters in deep embedding learning"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "mastering sketching adversarial augmentation for structured prediction"
    - "a neural representation of sketch drawings"
    - "3d sketching using multi view deep volumetric prediction"
    - "learning aligned cross modal representations from weakly aligned data"
    - "sketch based 3d shape retrieval using convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "convolutional humanoid animation via deformation"):
  with members:
    - "learning symmetric and low energy locomotion"
    - "synthesizing images of humans in unseen poses"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "everybody dance now"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "adam a method for stochastic optimization"
    - "automatic face reenactment"

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)