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: "graphonomy universal human parsing via graph transfer learning"
  Abstract: Prior highly-tuned human parsing models tend to fit towards each dataset in a specific domain or with discrepant label granularity, and can hardly be adapted to other human parsing tasks without extensive re-training. In this paper, we aim to learn a single universal human parsing model that can tackle all kinds of human parsing needs by unifying label annotations from different domains or at various levels of granularity. This poses many fundamental learning challenges, e.g. discovering underlying semantic structures among different label granularity, performing proper transfer learning across different image domains, and identifying and utilizing label redundancies across related tasks. #R##N#To address these challenges, we propose a new universal human parsing agent, named "Graphonomy", which incorporates hierarchical graph transfer learning upon the conventional parsing network to encode the underlying label semantic structures and propagate relevant semantic information. In particular, Graphonomy first learns and propagates compact high-level graph representation among the labels within one dataset via Intra-Graph Reasoning, and then transfers semantic information across multiple datasets via Inter-Graph Transfer. Various graph transfer dependencies (\eg, similarity, linguistic knowledge) between different datasets are analyzed and encoded to enhance graph transfer capability. By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity. Experimental results show Graphonomy effectively achieves the state-of-the-art results on three human parsing benchmarks as well as advantageous universal human parsing performance.

Connected through hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "graspnet a large scale clustered and densely annotated datase for object grasping"
    - "reasoning visual dialogs with structural and partial observations"
    - "understanding human gaze communication by spatio temporal graph reasoning"
    - "spgnet semantic prediction guidance for scene parsing"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "devil in the details towards accurate single and multiple human parsing"

Connected through hyperedge HE-2 (induced by source paper "peelnet textured 3d reconstruction of human body using single view rgb image"):
  with members:
    - "humanmeshnet polygonal mesh recovery of humans"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "detailed human shape estimation from a single image by hierarchical mesh deformation"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "layer structured 3d scene inference via view synthesis"
    - "3d human body reconstruction from a single image via volumetric regression"
    - "siclope silhouette based clothed people"

Connected through hyperedge HE-3 (induced by source paper "c dlinknet considering multi level semantic features for human parsing"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "deep learning for free hand sketch a survey"
    - "multi graph transformer for free hand sketch recognition"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised body part parsing with pose based part priors"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "synthesizing images of humans in unseen poses"
    - "instance level human parsing via part grouping network"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "human semantic parsing for person re identification"
    - "weakly and semi supervised panoptic segmentation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning to segment every thing"

Connected through hyperedge HE-5 (induced by source paper "learning semantic neural tree for human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "devil in the details towards accurate single and multiple human parsing"
    - "instance level human parsing via part grouping network"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deformable convnets v2 more deformable better results"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "macro micro adversarial network for human parsing"

Connected through hyperedge HE-6 (induced by source paper "grapy ml graph pyramid mutual learning for cross dataset human parsing"):
  with members:
    - "devil in the details towards accurate single and multiple human parsing"
    - "instance level human parsing via part grouping network"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "unified perceptual parsing for scene understanding"
    - "parsing r cnn for instance level human analysis"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "understanding humans in crowded scenes deep nested adversarial learning and a new benchmark for multi human parsing"
    - "macro micro adversarial network for human parsing"

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)