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: "understanding humans in crowded scenes deep nested adversarial learning and a new benchmark for multi human parsing"
  Abstract: Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc. To this end, models need to comprehensively perceive the semantic information and the differences between instances in a multi-human image, which is recently defined as the multi-human parsing task. In this paper, we present a new large-scale database "Multi-Human Parsing (MHP)" for algorithm development and evaluation, and advances the state-of-the-art in understanding humans in crowded scenes. MHP contains 25,403 elaborately annotated images with 58 fine-grained semantic category labels, involving 2-26 persons per image and captured in real-world scenes from various viewpoints, poses, occlusion, interactions and background. We further propose a novel deep Nested Adversarial Network (NAN) model for multi-human parsing. NAN consists of three Generative Adversarial Network (GAN)-like sub-nets, respectively performing semantic saliency prediction, instance-agnostic parsing and instance-aware clustering. These sub-nets form a nested structure and are carefully designed to learn jointly in an end-to-end way. NAN consistently outperforms existing state-of-the-art solutions on our MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.

Connected through hyperedge HE-1 (induced by source paper "graphonomy universal human parsing via graph transfer learning"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "iterative visual reasoning beyond convolutions"
    - "instance level human parsing via part grouping network"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "holistic instance level human parsing"

Connected through hyperedge HE-2 (induced by source paper "multi task human analysis in still images 2d 3d pose depth map and multi part segmentation"):
  with members:
    - "multi scale structure aware network for human pose estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "taskonomy disentangling task transfer learning"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-3 (induced by source paper "grapy ml graph pyramid mutual learning for cross dataset human parsing"):
  with members:
    - "graphonomy universal human parsing via graph transfer learning"
    - "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"
    - "macro micro adversarial network for human parsing"

Connected through hyperedge HE-4 (induced by source paper "instagan instance aware image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "weakly supervised instance segmentation using class peak response"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "deep sets"
    - "improving shape deformation in unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "parsing r cnn for instance level human analysis"):
  with members:
    - "panoptic segmentation"
    - "group normalization"
    - "ican instance centric attention network for human object interaction detection"
    - "instance level human parsing via part grouping network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "densepose dense human pose estimation in the wild"
    - "fssd feature fusion single shot multibox detector"

Connected through hyperedge HE-6 (induced by source paper "devil in the details towards accurate single and multiple human parsing"):
  with members:
    - "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"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "multiple human parsing in the wild"
    - "holistic instance level human parsing"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

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)