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: "multi label image recognition with graph convolutional networks"
  Abstract: The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important dependencies, we propose a multi-label classification model based on Graph Convolutional Network (GCN). The model builds a directed graph over the object labels, where each node (label) is represented by word embeddings of a label, and GCN is learned to map this label graph into a set of inter-dependent object classifiers. These classifiers are applied to the image descriptors extracted by another sub-net, enabling the whole network to be end-to-end trainable. Furthermore, we propose a novel re-weighted scheme to create an effective label correlation matrix to guide information propagation among the nodes in GCN. Experiments on two multi-label image recognition datasets show that our approach obviously outperforms other existing state-of-the-art methods. In addition, visualization analyses reveal that the classifiers learned by our model maintain meaningful semantic topology.

Connected through hyperedge HE-1 (induced by source paper "inducing optimal attribute representations for conditional gans"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "auglabel exploiting word representations to augment labels for face attribute classification"
    - "biphasic learning of gans for high resolution image to image translation"
    - "cgans with projection discriminator"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "graph neural networks a review of methods and applications"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"

Connected through hyperedge HE-2 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "exploring the limits of weakly supervised pretraining"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"

Connected through hyperedge HE-3 (induced by source paper "an ontology aware framework for audio event classification"):
  with members:
    - "improving image captioning by leveraging knowledge graphs"
    - "cross task learning for audio tagging sound event detection and spatial localization dcase 2019 baseline systems"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolutional networks for text classification"
    - "geometric deep learning going beyond euclidean data"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "cnn architectures for large scale audio classification"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "temporal reasoning graph for activity recognition"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "linkage based face clustering via graph convolution network"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "graph neural networks a review of methods and applications"
    - "learning discriminative video representations using adversarial perturbations"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-5 (induced by source paper "attkgcn attribute knowledge graph convolutional network for person re identification"):
  with members:
    - "learning context graph for person search"
    - "aanet attribute attention network for person re identifications"
    - "attributes aided part detection and refinement for person re identification"
    - "pedestrian re identification based on tree branch network with local and global learning"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "improving person re identification by attribute and identity learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-6 (induced by source paper "cross modality attention with semantic graph embedding for multi label classification"):
  with members:
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "approach for video classification with multi label on youtube 8m dataset"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label zero shot learning with structured knowledge graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "order free rnn with visual attention for multi label classification"
    - "attention clusters purely attention based local feature integration for video classification"
    - "skip rnn learning to skip state updates in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "multi label classification with label graph superimposing"):
  with members:
    - "centrality graph convolutional networks for skeleton based action recognition"
    - "long term feature banks for detailed video understanding"
    - "nextvlad an efficient neural network to aggregate frame level features for large scale video classification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "optimized skeleton based action recognition via sparsified graph regression"
    - "a baseline for multi label image classification using an ensemble of deep convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "a comparative study of high recall real time semantic segmentation based on swift factorized network"):
  with members:
    - "panoramic annular localizer tackling the variation challenges of outdoor localization using panoramic annular images and active deep descriptors"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "importance aware semantic segmentation with efficient pyramidal context network for navigational assistant systems"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"

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)