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: "pixel level domain transfer"
  Abstract: We present an image-conditional image generation model. The model transfers an input domain to a target domain in semantic level, and generates the target image in pixel level. To generate realistic target images, we employ the real/fake-discriminator as in Generative Adversarial Nets, but also introduce a novel domain-discriminator to make the generated image relevant to the input image. We verify our model through a challenging task of generating a piece of clothing from an input image of a dressed person. We present a high quality clothing dataset containing the two domains, and succeed in demonstrating decent results.

Connected through hyperedge HE-1 (induced by source paper "show attend and translate unsupervised image translation with self regularization and attention"):
  with members:
    - "few shot unsupervised image to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "ace adapting to changing environments for semantic segmentation"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "multimodal unsupervised image to image translation"
    - "reinforced continual learning"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "end to end incremental learning"

Connected through hyperedge HE-3 (induced by source paper "the six fronts of the generative adversarial networks"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"

Connected through hyperedge HE-4 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

Connected through hyperedge HE-5 (induced by source paper "preserving semantic and temporal consistency for unpaired video to video translation"):
  with members:
    - "inserting videos into videos"
    - "deep video inpainting"
    - "deep blind video decaptioning by temporal aggregation and recurrence"
    - "frame recurrent video super resolution"
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"

Connected through hyperedge HE-6 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised creation of parameterized avatars"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "predicting the future a jointly learnt model for action anticipation"):
  with members:
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "deep cosine metric learning for person re identification"
    - "convolutional sequence to sequence model for human dynamics"
    - "multi level sequence gan for group activity recognition"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "action anticipation by predicting future dynamic images"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-8 (induced by source paper "points2pix 3d point cloud to image translation using conditional generative adversarial networks"):
  with members:
    - "yolov3 an incremental improvement"
    - "point cloud gan"
    - "complex yolo real time 3d object detection on point clouds"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "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)