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: "learning deep sketch abstraction"
  Abstract: Human free-hand sketches have been studied in various contexts including sketch recognition, synthesis and fine-grained sketch-based image retrieval (FG-SBIR). A fundamental challenge for sketch analysis is to deal with drastically different human drawing styles, particularly in terms of abstraction level. In this work, we propose the first stroke-level sketch abstraction model based on the insight of sketch abstraction as a process of trading off between the recognizability of a sketch and the number of strokes used to draw it. Concretely, we train a model for abstract sketch generation through reinforcement learning of a stroke removal policy that learns to predict which strokes can be safely removed without affecting recognizability. We show that our abstraction model can be used for various sketch analysis tasks including: (1) modeling stroke saliency and understanding the decision of sketch recognition models, (2) synthesizing sketches of variable abstraction for a given category, or reference object instance in a photo, and (3) training a FG-SBIR model with photos only, bypassing the expensive photo-sketch pair collection step.

Connected through hyperedge HE-1 (induced by source paper "deep self supervised representation learning for free hand sketch"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "zero reference deep curve estimation for low light image enhancement"
    - "generative adversarial zero shot relational learning for knowledge graphs"
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "multi graph transformer for free hand sketch recognition"
    - "learning for multi model and multi type fitting"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (induced by source paper "sketch less for more on the fly fine grained sketch based image retrieval"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "semantic aware knowledge preservation for zero shot sketch based image retrieval"
    - "livesketch query perturbations for guided sketch based visual search"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "interactive sketch fill multiclass sketch to image translation"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "pluralistic image completion"
    - "goal driven sequential data abstraction"

Connected through hyperedge HE-3 (induced by source paper "synthesizing human like sketches from natural images using a conditional convolutional decoder"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "unsupervised learning of depth and ego motion from video"
    - "a neural representation of sketch drawings"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-4 (induced by source paper "fine grained instance level sketch based video retrieval"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "deep self supervised representation learning for free hand sketch"
    - "livesketch query perturbations for guided sketch based visual search"
    - "deep zero shot learning for scene sketch"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "multi fiber networks for video recognition"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "spatio temporal channel correlation networks for action classification"

Connected through hyperedge HE-5 (induced by source paper "sketchformer transformer based representation for sketched structure"):
  with members:
    - "livesketch query perturbations for guided sketch based visual search"
    - "transformer xl attentive language models beyond a fixed length context"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "image transformer"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "sketch r2cnn an attentive network for vector sketch recognition"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "goal driven sequential data abstraction"):
  with members:
    - "game of sketches deep recurrent models of pictionary style word guessing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "dtr gan dilated temporal relational adversarial network for video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "sequential dual deep learning with shape and texture features for sketch recognition"
    - "neural extractive summarization with side information"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-7 (induced by source paper "semi heterogeneous three way joint embedding network for sketch based image retrieval"):
  with members:
    - "person re identification by semantic region representation and topology constraint"
    - "multi attention multi class constraint for fine grained image recognition"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "harmonious attention network for person re identification"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "asymmetric feature maps with application to sketch based retrieval"
    - "squeeze and excitation networks"

Connected through hyperedge HE-8 (induced by source paper "deep zero shot learning for scene sketch"):
  with members:
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "a zero shot framework for sketch based image retrieval"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "generalized zero shot recognition based on visually semantic embedding"
    - "sketchyscene richly annotated scene sketches"
    - "unsupervised person re identification by deep learning tracklet association"
    - "semantic autoencoder for zero shot learning"
    - "attribute recognition by joint recurrent learning of context and correlation"

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)