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: "sketchyscene richly annotated scene sketches"
  Abstract: We contribute the first large-scale dataset of scene sketches, SketchyScene, with the goal of advancing research on sketch understanding at both the object and scene level. The dataset is created through a novel and carefully designed crowdsourcing pipeline, enabling users to efficiently generate large quantities of realistic and diverse scene sketches. SketchyScene contains more than 29,000 scene-level sketches, 7,000+ pairs of scene templates and photos, and 11,000+ object sketches. All objects in the scene sketches have ground-truth semantic and instance masks. The dataset is also highly scalable and extensible, easily allowing augmenting and/or changing scene composition. We demonstrate the potential impact of SketchyScene by training new computational models for semantic segmentation of scene sketches and showing how the new dataset enables several applications including image retrieval, sketch colorization, editing, and captioning, etc. The dataset and code can be found at this https URL.

Connected through hyperedge HE-1 (induced by source paper "deep semantic parsing of freehand sketches with homogeneous transformation soft weighted loss and staged learning"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "asymmetric feature maps with application to sketch based retrieval"
    - "random erasing data augmentation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "loss max pooling for semantic image segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep zero shot learning for scene sketch"):
  with members:
    - "learning deep sketch abstraction"
    - "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "sketchforme composing sketched scenes from text descriptions for interactive applications"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "attention is all you need"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a neural representation of sketch drawings"
    - "how2sketch generating easy to follow tutorials for sketching 3d objects"
    - "generative adversarial text to image synthesis"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "lucss language based user customized colourization of scene sketches"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "texturegan controlling deep image synthesis with texture patches"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "outline colorization through tandem adversarial networks"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-5 (induced by source paper "sketch r2cnn an attentive network for vector sketch recognition"):
  with members:
    - "learning deep sketch abstraction"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "learning to sketch with shortcut cycle consistency"
    - "fast sketch segmentation and labeling with deep learning"
    - "neural 3d mesh renderer"
    - "a neural representation of sketch drawings"
    - "dual attention networks for multimodal reasoning and matching"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-6 (induced by source paper "scene categorization from contours medial axis based salience measures"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)