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: "fast sketch segmentation and labeling with deep learning"
  Abstract: We present a simple and efficient method based on deep learning to automatically decompose sketched objects into semantically valid parts. We train a deep neural network to transfer existing segmentations and labelings from 3D models to freehand sketches without requiring numerous well-annotated sketches as training data. The network takes the binary image of a sketched object as input and produces a corresponding segmentation map with per-pixel labelings as output. A subsequent post-process procedure with multi-label graph cuts further refines the segmentation and labeling result. We validate our proposed method on two sketch datasets. Experiments show that our method outperforms the state-of-the-art method in terms of segmentation and labeling accuracy and is significantly faster, enabling further integration in interactive drawing systems. We demonstrate the efficiency of our method in a sketch-based modeling application that automatically transforms input sketches into 3D models by part assembly.

Connected through hyperedge HE-1 (induced by source paper "sketchdesc learning local sketch descriptors for multi view correspondence"):
  with members:
    - "sketchzooms deep multi view descriptors for matching line drawings"
    - "hierarchical deep stereo matching on high resolution images"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "from handcrafted to deep local invariant features"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "sketch r2cnn an attentive network for vector sketch recognition"
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "repeatability is not enough learning affine regions via discriminability"

Connected through hyperedge HE-2 (induced by source paper "sketchgcn semantic sketch segmentation with graph convolutional networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning to sketch with shortcut cycle consistency"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dynamic graph cnn for learning on point clouds"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "stroke based sketched symbol reconstruction and segmentation"):
  with members:
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "a neural representation of sketch drawings"
    - "variational neural machine translation"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "generating sentences from a continuous space"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "draw a recurrent neural network for image generation"

Connected through hyperedge HE-4 (induced by source paper "can i teach a robot to replicate a line art"):
  with members:
    - "stroke based sketched symbol reconstruction and segmentation"
    - "a neural representation of sketch drawings"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "sketch r2cnn an attentive network for vector sketch recognition"):
  with members:
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "sketchyscene richly annotated scene sketches"
    - "neural 3d mesh renderer"
    - "a neural representation of sketch drawings"
    - "dual attention networks for multimodal reasoning and matching"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

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.GR(Graphics)