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: "devil in the details towards accurate single and multiple human parsing"
  Abstract: Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an efficient and elegant way. In this paper, we identify several useful properties, including feature resolution, global context information and edge details, and perform rigorous analyses to reveal how to leverage them to benefit the human parsing task. The advantages of these useful properties finally result in a simple yet effective Context Embedding with Edge Perceiving (CE2P) framework for single human parsing. Our CE2P is end-to-end trainable and can be easily adopted for conducting multiple human parsing. Benefiting the superiority of CE2P, we achieved the 1st places on all three human parsing benchmarks. Without any bells and whistles, we achieved 56.50\% (mIoU), 45.31\% (mean $AP^r$) and 33.34\% ($AP^p_{0.5}$) in LIP, CIHP and MHP v2.0, which outperform the state-of-the-arts more than 2.06\%, 3.81\% and 1.87\%, respectively. We hope our CE2P will serve as a solid baseline and help ease future research in single/multiple human parsing. Code has been made available at \url{this https URL}.

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"
    - "graphonomy universal human parsing via graph transfer learning"
    - "spgnet semantic prediction guidance for scene parsing"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-2 (induced by source paper "object contextual representations for semantic segmentation"):
  with members:
    - "learning compositional neural information fusion for human parsing"
    - "acfnet attentional class feature network for semantic segmentation"
    - "dual graph convolutional network for semantic segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "global aggregation then local distribution in fully convolutional networks"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "high resolution representations for labeling pixels and regions"

Connected through hyperedge HE-3 (induced by source paper "interlaced sparse self attention for semantic segmentation"):
  with members:
    - "deeperlab single shot image parser"
    - "generating long sequences with sparse transformers"
    - "semantic correlation promoted shape variant context for segmentation"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "look into person joint body parsing pose estimation network and a new benchmark"

Connected through hyperedge HE-4 (induced by source paper "visual person understanding through multi task and multi dataset learning"):
  with members:
    - "improved person re identification based on saliency and semantic parsing with deep neural network models"
    - "casenet content adaptive scale interaction networks for scene parsing"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "panoptic feature pyramid networks"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"
    - "group normalization"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"

Connected through hyperedge HE-5 (induced by source paper "casenet content adaptive scale interaction networks for scene parsing"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "context aware visual policy network for sequence level image captioning"
    - "unified perceptual parsing for scene understanding"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-6 (induced by source paper "beyond human parts dual part aligned representations for person re identification"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "aanet attribute attention network for person re identifications"
    - "object contextual representations for semantic segmentation"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "improving person re identification by attribute and identity learning"
    - "attribute aware attention model for fine grained representation learning"
    - "multi level factorisation net for person re identification"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-7 (induced by source paper "self correction for human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ocnet object context network for scene parsing"
    - "instance level human parsing via part grouping network"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "averaging weights leads to wider optima and better generalization"
    - "macro micro adversarial network for human parsing"

Connected through hyperedge HE-8 (induced by source paper "anti confusing region aware network for human pose estimation"):
  with members:
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "quantized densely connected u nets for efficient landmark localization"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "look into person self supervised structure sensitive learning and a new benchmark 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)