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: "revisiting cross modal retrieval"
  Abstract: This paper proposes a cross-modal retrieval system that leverages on image and text encoding. Most multimodal architectures employ separate networks for each modality to capture the semantic relationship between them. However, in our work image-text encoding can achieve comparable results in terms of cross-modal retrieval without having to use a separate network for each modality. We show that text encodings can capture semantic relationships between multiple modalities. In our knowledge, this work is the first of its kind in terms of employing a single network and fused image-text embedding for cross-modal retrieval. We evaluate our approach on two famous multimodal datasets: MS-COCO and Flickr30K.

Connected through hyperedge HE-1 (induced by source paper "aiding intra text representations with visual context for multimodal named entity recognition"):
  with members:
    - "learning word vectors for 157 languages"
    - "multimodal named entity recognition for short social media posts"
    - "efficient large scale multi modal classification"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a multi task approach for named entity recognition in social media data"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "dual attention networks for multimodal reasoning and matching"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-2 (induced by source paper "multitask text to visual embedding with titles and clickthrough data"):
  with members:
    - "learning word vectors for 157 languages"
    - "webly supervised joint embedding for cross modal image text retrieval"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "unified deep supervised domain adaptation and generalization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning inward scaled hypersphere embedding exploring projections in higher dimensions"):
  with members:
    - "git loss for deep face recognition"
    - "random erasing data augmentation"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "image text multi modal representation learning by adversarial backpropagation"
    - "wide residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "learning deep structure preserving image text embeddings"

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)