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: "mastering sketching adversarial augmentation for structured prediction"
  Abstract: We present an integral framework for training sketch simplification networks that convert challenging rough sketches into clean line drawings. Our approach augments a simplification network with a discriminator network, training both networks jointly so that the discriminator network discerns whether a line drawing is a real training data or the output of the simplification network, which in turn tries to fool it. This approach has two major advantages. First, because the discriminator network learns the structure in line drawings, it encourages the output sketches of the simplification network to be more similar in appearance to the training sketches. Second, we can also train the simplification network with additional unsupervised data, using the discriminator network as a substitute teacher. Thus, by adding only rough sketches without simplified line drawings, or only line drawings without the original rough sketches, we can improve the quality of the sketch simplification. We show how our framework can be used to train models that significantly outperform the state of the art in the sketch simplification task, despite using the same architecture for inference. We additionally present an approach to optimize for a single image, which improves accuracy at the cost of additional computation time. Finally, we show that, using the same framework, it is possible to train the network to perform the inverse problem, i.e., convert simple line sketches into pencil drawings, which is not possible using the standard mean squared error loss. We validate our framework with two user tests, where our approach is preferred to the state of the art in sketch simplification 92.3% of the time and obtains 1.2 more points on a scale of 1 to 5.

Connected through hyperedge HE-1 (induced by source paper "learning to shade hand drawn sketches"):
  with members:
    - "im2pencil controllable pencil illustration from photographs"
    - "tag2pix line art colorization using text tag with secat and changing loss"
    - "single image portrait relighting"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comicolorization semi automatic manga colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "deep vectorization of technical drawings"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "stroke based sketched symbol reconstruction and segmentation"
    - "vectorization of line drawings via polyvector fields"
    - "a complete hand drawn sketch vectorization framework"
    - "vectordefense vectorization as a defense to adversarial examples"
    - "abc a big cad model dataset for geometric deep learning"
    - "attention is all you need"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural network based topology optimization cnn to by estimating sensitivity of compliance from material distribution"):
  with members:
    - "3d topology optimization using convolutional neural networks"
    - "deep reinforcement learning that matters"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "argan attentive recurrent generative adversarial network for shadow detection and removal"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "dynamic meta embeddings for improved sentence representations"
    - "spectral normalization for generative adversarial networks"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "direction aware spatial context features for shadow detection and removal"
    - "a d net training a shadow detector with adversarial shadow attenuation"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "direction aware spatial context features for shadow detection"

Connected through hyperedge HE-5 (induced by source paper "semantic example guided image to image translation"):
  with members:
    - "asymmetric gan for unpaired image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generative image inpainting with contextual attention"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "tag2pix line art colorization using text tag with secat and changing loss"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "learning to color from language"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "language based image editing with recurrent attentive models"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "comicolorization semi automatic manga colorization"

Connected through hyperedge HE-7 (induced by source paper "sketchzooms deep multi view descriptors for matching line drawings"):
  with members:
    - "neural best buddies sparse cross domain correspondence"
    - "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"
    - "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-8 (induced by source paper "learning image to image translation using paired and unpaired training samples"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "conditional image to image translation"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "perceptual adversarial networks for image to image transformation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "real time user guided image colorization with learned deep priors"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)