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: "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
  Abstract: The ability to semantically interpret hand-drawn line sketches, although very challenging, can pave way for novel applications in multimedia. We propose SketchParse, the first deep-network architecture for fully automatic parsing of freehand object sketches. SketchParse is configured as a two-level fully convolutional network. The first level contains shared layers common to all object categories. The second level contains a number of expert sub-networks. Each expert specializes in parsing sketches from object categories which contain structurally similar parts. Effectively, the two-level configuration enables our architecture to scale up efficiently as additional categories are added. We introduce a router layer which (i) relays sketch features from shared layers to the correct expert (ii) eliminates the need to manually specify object category during inference. To bypass laborious part-level annotation, we sketchify photos from semantic object-part image datasets and use them for training. Our architecture also incorporates object pose prediction as a novel auxiliary task which boosts overall performance while providing supplementary information regarding the sketch. We demonstrate SketchParse's abilities (i) on two challenging large-scale sketch datasets (ii) in parsing unseen, semantically related object categories (iii) in improving fine-grained sketch-based image retrieval. As a novel application, we also outline how SketchParse's output can be used to generate caption-style descriptions for hand-drawn sketches.

Connected through hyperedge HE-1 (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"
    - "learning to sketch with shortcut cycle consistency"
    - "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"

Connected through hyperedge HE-2 (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"
    - "sketchyscene richly annotated scene sketches"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning task relatedness in multi task learning for images in context"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"
    - "an overview of multi task learning in deep neural networks"
    - "omniart multi task deep learning for artistic data analysis"
    - "deep cross residual learning for multitask visual recognition"
    - "distributed multi task relationship learning"
    - "deep multi task representation learning a tensor factorisation approach"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"

Connected through hyperedge HE-4 (induced by source paper "teaching gans to sketch in vector format"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "sketch pix2seq a model to generate sketches of multiple categories"
    - "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"
    - "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-5 (induced by source paper "sketch specific data augmentation for freehand sketch recognition"):
  with members:
    - "object category understanding via eye fixations on freehand sketches"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enabling my robot to play pictionary recurrent neural networks for sketch recognition"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "scribbler controlling deep image synthesis with sketch and color"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "multi view convolutional neural networks for 3d shape recognition"

Connected through hyperedge HE-6 (induced by source paper "fast sketch segmentation and labeling with deep learning"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for 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)