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: "free hand sketch synthesis with deformable stroke models"
  Abstract: We present a generative model which can automatically summarize the stroke composition of free-hand sketches of a given category. When our model is fit to a collection of sketches with similar poses, it discovers and learns the structure and appearance of a set of coherent parts, with each part represented by a group of strokes. It represents both consistent (topology) as well as diverse aspects (structure and appearance variations) of each sketch category. Key to the success of our model are important insights learned from a comprehensive study performed on human stroke data. By fitting this model to images, we are able to synthesize visually similar and pleasant free-hand sketches.

Connected through hyperedge HE-1 (induced by source paper "scoot a perceptual metric for facial sketches"):
  with members:
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "nima neural image assessment"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "link the head to the beak zero shot learning from noisy text description at part precision"
    - "a multilayer based framework for online background subtraction with freely moving cameras"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "random sampling for fast face sketch synthesis"

Connected through hyperedge HE-2 (induced by source paper "deep factorised inverse sketching"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning deep sketch abstraction"
    - "zero shot sketch image hashing"
    - "learning to sketch with shortcut cycle consistency"
    - "sketch a classifier sketch based photo classifier generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "learning deep sketch abstraction"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a neural representation of sketch drawings"
    - "perceptual losses for real time style transfer and super resolution"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "scribbler controlling deep image synthesis with sketch and color"

Connected through hyperedge HE-4 (induced by source paper "sketchmate deep hashing for million scale human sketch retrieval"):
  with members:
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "a neural representation of sketch drawings"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "deep residual learning for image recognition"
    - "sketch a net that beats humans"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "cross modal subspace learning for fine grained sketch based image retrieval"):
  with members:
    - "a comprehensive survey on cross modal retrieval"
    - "compound rank k projections for bilinear analysis"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "multimodal sparse coding for event detection"
    - "sketch a net that beats humans"
    - "a convex formulation for spectral shrunk clustering"
    - "robust recovery of subspace structures by low rank representation"

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)