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 text multi modal representation learning by adversarial backpropagation"
  Abstract: We present novel method for image-text multi-modal representation learning. In our knowledge, this work is the first approach of applying adversarial learning concept to multi-modal learning and not exploiting image-text pair information to learn multi-modal feature. We only use category information in contrast with most previous methods using image-text pair information for multi-modal embedding. In this paper, we show that multi-modal feature can be achieved without image-text pair information and our method makes more similar distribution with image and text in multi-modal feature space than other methods which use image-text pair information. And we show our multi-modal feature has universal semantic information, even though it was trained for category prediction. Our model is end-to-end backpropagation, intuitive and easily extended to other multi-modal learning work.

Connected through hyperedge HE-1 (induced by source paper "do cross modal systems leverage semantic relationships"):
  with members:
    - "seeing voices and hearing faces cross modal biometric matching"
    - "voxceleb2 deep speaker recognition"
    - "learning cross modal deep embeddings for multi object image retrieval using text and sketch"
    - "learning two branch neural networks for image text matching tasks"
    - "dual path convolutional image text embedding with instance loss"
    - "visually aligned word embeddings for improving zero shot learning"
    - "look listen and learn"
    - "representations of language in a model of visually grounded speech signal"

Connected through hyperedge HE-2 (induced by source paper "multimodal representation model based on graph based rank fusion"):
  with members:
    - "fusion vectors embedding graph fusions for efficient unsupervised rank aggregation"
    - "unsupervised graph based rank aggregation for improved retrieval"
    - "do better imagenet models transfer better"
    - "tensor fusion network for multimodal sentiment analysis"
    - "multimodal machine learning a survey and taxonomy"
    - "learning transferable architectures for scalable image recognition"
    - "deep residual learning for image recognition"
    - "visual word2vec vis w2v learning visually grounded word embeddings using abstract scenes"

Connected through hyperedge HE-3 (induced by source paper "learning inward scaled hypersphere embedding exploring projections in higher dimensions"):
  with members:
    - "revisiting cross modal retrieval"
    - "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"
    - "wide residual networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-4 (induced by source paper "jtav jointly learning social media content representation by fusing textual acoustic and visual features"):
  with members:
    - "a tutorial on deep learning for music information retrieval"
    - "image2song song retrieval via bridging image content and lyric words"
    - "visual and textual sentiment analysis using deep fusion convolutional neural networks"
    - "enriching word vectors with subword information"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "social media image analysis for public health"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a new evaluation protocol and benchmarking results for extendable cross media retrieval"):
  with members:
    - "how should we evaluate supervised hashing"
    - "person re identification past present and future"
    - "deep cross modal hashing"
    - "sift meets cnn a decade survey of instance retrieval"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "explain images with multimodal recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "content based video music retrieval using soft intra modal structure constraint"):
  with members:
    - "learning two branch neural networks for image text matching tasks"
    - "sspp dan deep domain adaptation network for face recognition with single sample per person"
    - "recognizing dynamic scenes with deep dual descriptor based on key frames and key segments"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "youtube 8m a large scale video classification benchmark"
    - "deep pyramidal residual networks"
    - "convolutional recurrent neural networks for music classification"
    - "learning deep structure preserving image text embeddings"

Connected through hyperedge HE-7 (induced by source paper "adversarial multi task learning for text classification"):
  with members:
    - "a structured self attentive sentence embedding"
    - "unsupervised cross domain image generation"
    - "cross stitch networks for multi task learning"
    - "domain separation networks"
    - "multi task sequence to sequence learning"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"

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)