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: "image labeling on a network using social network metadata for image classification"
  Abstract: Large-scale image retrieval benchmarks invariably consist of images from the Web. Many of these benchmarks are derived from online photo sharing networks, like Flickr, which in addition to hosting images also provide a highly interactive social community. Such communities generate rich metadata that can naturally be harnessed for image classification and retrieval. Here we study four popular benchmark datasets, extending them with social-network metadata, such as the groups to which each image belongs, the comment thread associated with the image, who uploaded it, their location, and their network of friends. Since these types of data are inherently relational, we propose a model that explicitly accounts for the interdependencies between images sharing common properties. We model the task as a binary labeling problem on a network, and use structured learning techniques to learn model parameters. We find that social-network metadata are useful in a variety of classification tasks, in many cases outperforming methods based on image content.

Connected through hyperedge HE-1 (induced by source paper "factorized graph representations for semi supervised learning from sparse data"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "a general framework for estimating graphlet statistics via random walk"
    - "semi supervised classification with graph convolutional networks"
    - "graph based semi supervised learning for relational networks"
    - "semi supervised learning with heterophily"
    - "localization and centrality in networks"
    - "linearized and single pass belief propagation"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-2 (induced by source paper "maplur exploring a new paradigm for estimating air pollution using deep learning on map images"):
  with members:
    - "rewarding coreference resolvers for being consistent with world knowledge"
    - "exploring the limits of weakly supervised pretraining"
    - "fine tuned language models for text classification"
    - "self normalizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "using user generated online photos to estimate and monitor air pollution in major cities"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "fast sequence based embedding with diffusion graphs"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "context aware image annotation in active learning"):
  with members:
    - "how does tweet difficulty affect labeling performance of annotators"
    - "improving image classification with location context"
    - "deep convolutional ranking for multilabel image annotation"

Connected through hyperedge HE-5 (induced by source paper "hyperlearn a distributed approach for representation learning in datasets with many modalities"):
  with members:
    - "social anchor unit graph regularized tensor completion for large scale image retagging"
    - "videos as space time region graphs"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "omniart multi task deep learning for artistic data analysis"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "vital a visual interpretation on text with adversarial learning for image labeling"):
  with members:
    - "unsupervised textual grounding linking words to image concepts"
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "conditional image text embedding networks"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "multichannel variable size convolution for sentence classification"
    - "learning what and where to draw"
    - "plug play generative networks conditional iterative generation of images in latent space"

Connected through hyperedge HE-7 (induced by source paper "a cnn rnn framework for image annotation from visual cues and social network metadata"):
  with members:
    - "modeling visual context is key to augmenting object detection datasets"
    - "deep neural networks in fully connected crf for image labeling with social network metadata"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "automatic image annotation via label transfer in the semantic space"
    - "semantic regularisation for recurrent image annotation"
    - "cnn rnn a unified framework for multi label image classification"
    - "webly supervised learning of convolutional networks"
    - "learning structured inference neural networks with label relations"

Connected through hyperedge HE-8 (induced by source paper "plugin networks for inference under partial evidence"):
  with members:
    - "feedback prop convolutional neural network inference under partial evidence"
    - "priming neural networks"
    - "learnable pooling with context gating for video classification"
    - "learning multiple visual domains with residual adapters"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "film visual reasoning with a general conditioning layer"
    - "learning structured inference neural networks with label relations"
    - "deep residual learning for image recognition"

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)