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 to sketch with shortcut cycle consistency"
  Abstract: To see is to sketch -- free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process. This is an extremely challenging task because the photo and sketch domains differ significantly. Furthermore, human sketches exhibit various levels of sophistication and abstraction even when depicting the same object instance in a reference photo. This means that even if photo-sketch pairs are available, they only provide weak supervision signal to learn a translation model. Compared with existing supervised approaches that solve the problem of D(E(photo)) -> sketch, where E($\cdot$) and D($\cdot$) denote encoder and decoder respectively, we take advantage of the inverse problem (e.g., D(E(sketch)) -> photo), and combine with the unsupervised learning tasks of within-domain reconstruction, all within a multi-task learning framework. Compared with existing unsupervised approaches based on cycle consistency (i.e., D(E(D(E(photo)))) -> photo), we introduce a shortcut consistency enforced at the encoder bottleneck (e.g., D(E(photo)) -> photo) to exploit the additional self-supervision. Both qualitative and quantitative results show that the proposed model is superior to a number of state-of-the-art alternatives. We also show that the synthetic sketches can be used to train a better fine-grained sketch-based image retrieval (FG-SBIR) model, effectively alleviating the problem of sketch data scarcity.

Connected through hyperedge HE-1 (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-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"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "dynamic graph cnn for learning on point clouds"
    - "fast sketch segmentation and labeling with deep learning"
    - "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 "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"
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "sketch r2cnn an attentive network for vector sketch recognition"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "monocular 3d object detection with pseudo lidar point cloud"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "capturing object detection uncertainty in multi layer grid maps"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "3d object detection using scale invariant and feature reweighting networks"
    - "ipod intensive point based object detector for point cloud"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "deep ordinal regression network for monocular depth estimation"

Connected through hyperedge HE-5 (induced by source paper "learning to paint with model based deep reinforcement learning"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "investigating human priors for playing video games"
    - "learning to run challenge solutions adapting reinforcement learning methods for neuromusculoskeletal environments"
    - "recurrent world models facilitate policy evolution"
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "improved training of wasserstein gans"
    - "proximal policy optimization algorithms"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-6 (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"
    - "learning deep sketch abstraction"
    - "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"
    - "asymmetric feature maps with application to sketch based retrieval"
    - "squeeze and excitation networks"

Connected through hyperedge HE-7 (induced by source paper "an unpaired sketch to photo translation model"):
  with members:
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "free form image inpainting with gated convolution"
    - "deep factorised inverse sketching"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep sketch hashing fast free hand sketch based image retrieval"
    - "a neural representation of sketch drawings"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-8 (induced by source paper "photo sketching inferring contour drawings from images"):
  with members:
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a neural representation of sketch drawings"
    - "perceptual losses for real time style transfer and super resolution"
    - "stochastic multiple choice learning for training diverse deep ensembles"
    - "image to image translation with conditional adversarial networks"
    - "holistically nested edge detection"
    - "deep residual learning for image recognition"

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)