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: "semantic object parsing with local global long short term memory"
  Abstract: Semantic object parsing is a fundamental task for understanding objects in detail in computer vision community, where incorporating multi-level contextual information is critical for achieving such fine-grained pixel-level recognition. Prior methods often leverage the contextual information through post-processing predicted confidence maps. In this work, we propose a novel deep Local-Global Long Short-Term Memory (LG-LSTM) architecture to seamlessly incorporate short-distance and long-distance spatial dependencies into the feature learning over all pixel positions. In each LG-LSTM layer, local guidance from neighboring positions and global guidance from the whole image are imposed on each position to better exploit complex local and global contextual information. Individual LSTMs for distinct spatial dimensions are also utilized to intrinsically capture various spatial layouts of semantic parts in the images, yielding distinct hidden and memory cells of each position for each dimension. In our parsing approach, several LG-LSTM layers are stacked and appended to the intermediate convolutional layers to directly enhance visual features, allowing network parameters to be learned in an end-to-end way. The long chains of sequential computation by stacked LG-LSTM layers also enable each pixel to sense a much larger region for inference benefiting from the memorization of previous dependencies in all positions along all dimensions. Comprehensive evaluations on three public datasets well demonstrate the significant superiority of our LG-LSTM over other state-of-the-art methods.

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 "learning compositional neural information fusion for human parsing"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "instance level human parsing via part grouping network"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "relational inductive biases deep learning and graph networks"
    - "learning human object interactions by graph parsing neural networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "macro micro adversarial network for human parsing"
    - "holistic instance level human parsing"

Connected through hyperedge HE-3 (induced by source paper "deep portrait image completion and extrapolation"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "everybody dance now"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "generative face completion"
    - "a generative model of people in clothing"

Connected through hyperedge HE-4 (induced by source paper "semantic correlation promoted shape variant context for segmentation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "aanet attribute attention network for person re identifications"
    - "scene graph generation with external knowledge and image reconstruction"
    - "towards robust curve text detection with conditional spatial expansion"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "a network structure to explicitly reduce confusion errors in semantic segmentation"
    - "a classification refinement strategy for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "boundary aware feature propagation for scene segmentation"):
  with members:
    - "unpaired image captioning via scene graph alignments"
    - "feature boosting network for 3d pose estimation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "adaptive context network for scene parsing"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "cross domain complementary learning with synthetic data for multi person part segmentation"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning to adapt structured output space for semantic segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "densepose dense human pose estimation in the wild"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-7 (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"
    - "understanding humans in crowded scenes deep nested adversarial learning and a new benchmark for multi human parsing"

Connected through hyperedge HE-8 (induced by source paper "self correction 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"
    - "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"

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)