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: "sketch pix2seq a model to generate sketches of multiple categories"
  Abstract: Sketch is an important media for human to communicate ideas, which reflects the superiority of human intelligence. Studies on sketch can be roughly summarized into recognition and generation. Existing models on image recognition failed to obtain satisfying performance on sketch classification. But for sketch generation, a recent study proposed a sequence-to-sequence variational-auto-encoder (VAE) model called sketch-rnn which was able to generate sketches based on human inputs. The model achieved amazing results when asked to learn one category of object, such as an animal or a vehicle. However, the performance dropped when multiple categories were fed into the model. Here, we proposed a model called sketch-pix2seq which could learn and draw multiple categories of sketches. Two modifications were made to improve the sketch-rnn model: one is to replace the bidirectional recurrent neural network (BRNN) encoder with a convolutional neural network(CNN); the other is to remove the Kullback-Leibler divergence from the objective function of VAE. Experimental results showed that models with CNN encoders outperformed those with RNN encoders in generating human-style sketches. Visualization of the latent space illustrated that the removal of KL-divergence made the encoder learn a posterior of latent space that reflected the features of different categories. Moreover, the combination of CNN encoder and removal of KL-divergence, i.e., the sketch-pix2seq model, had better performance in learning and generating sketches of multiple categories and showed promising results in creativity tasks.

Connected through hyperedge HE-1 (induced by source paper "learning to paint with model based deep reinforcement learning"):
  with members:
    - "learning to sketch with shortcut cycle consistency"
    - "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"
    - "improved training of wasserstein gans"
    - "proximal policy optimization algorithms"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-2 (induced by source paper "teaching gans to sketch in vector format"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "object category understanding via eye fixations on freehand sketches"
    - "image generation from sketch constraint using contextual gan"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "a neural representation of sketch drawings"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "enabling my robot to play pictionary recurrent neural networks for sketch recognition"

Connected through hyperedge HE-3 (induced by source paper "stroke based sketched symbol reconstruction and segmentation"):
  with members:
    - "fast sketch segmentation and labeling with deep learning"
    - "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 "3d shape synthesis for conceptual design and optimization using variational autoencoders"):
  with members:
    - "beziergan automatic generation of smooth curves from interpretable low dimensional parameters"
    - "neural networks for topology optimization"
    - "learning a hierarchical latent variable model of 3d shapes"
    - "matterport3d learning from rgb d data in indoor environments"
    - "orientation boosted voxel nets for 3d object recognition"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "fpnn field probing neural networks for 3d data"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "learning to sketch with shortcut cycle consistency"):
  with members:
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "cross modal subspace learning for fine grained sketch based image retrieval"
    - "fader networks manipulating images by sliding attributes"
    - "a neural representation of sketch drawings"
    - "dualgan unsupervised dual learning for image to image translation"
    - "instance normalization the missing ingredient for fast stylization"
    - "convolutional sketch inversion"

Connected through hyperedge HE-6 (induced by source paper "sketchyscene richly annotated scene sketches"):
  with members:
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "sketch a classifier sketch based photo classifier generation"
    - "smart sparse contours to represent and edit images"
    - "a neural representation of sketch drawings"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-7 (induced by source paper "computational decomposition of style for controllable and enhanced style transfer"):
  with members:
    - "learning to sketch with shortcut cycle consistency"
    - "neural style transfer a review"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "content aware neural style transfer"
    - "controlling perceptual factors in neural style transfer"

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)