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: "visual semantic reasoning for image text matching"
  Abstract: Image-text matching has been a hot research topic bridging the vision and language areas. It remains challenging because the current representation of image usually lacks global semantic concepts as in its corresponding text caption. To address this issue, we propose a simple and interpretable reasoning model to generate visual representation that captures key objects and semantic concepts of a scene. Specifically, we first build up connections between image regions and perform reasoning with Graph Convolutional Networks to generate features with semantic relationships. Then, we propose to use the gate and memory mechanism to perform global semantic reasoning on these relationship-enhanced features, select the discriminative information and gradually generate the representation for the whole scene. Experiments validate that our method achieves a new state-of-the-art for the image-text matching on MS-COCO and Flickr30K datasets. It outperforms the current best method by 6.8% relatively for image retrieval and 4.8% relatively for caption retrieval on MS-COCO (Recall@1 using 1K test set). On Flickr30K, our model improves image retrieval by 12.6% relatively and caption retrieval by 5.8% relatively (Recall@1). Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "cops ref a new dataset and task on compositional referring expression comprehension"):
  with members:
    - "improving referring expression grounding with cross modal attention guided erasing"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "binary image selection bison interpretable evaluation of visual grounding"
    - "cross modal self attention network for referring image segmentation"
    - "neural sequential phrase grounding seqground"
    - "cross modal relationship inference for grounding referring expressions"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "visual coreference resolution in visual dialog using neural module networks"

Connected through hyperedge HE-2 (induced by source paper "adaptive offline quintuplet loss for image text matching"):
  with members:
    - "expressing objects just like words recurrent visual embedding for image text matching"
    - "saliency guided attention network for image sentence matching"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "hal improved text image matching by mitigating visual semantic hubs"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "identity aware textual visual matching with latent co attention"
    - "dual path convolutional image text embedding with instance loss"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning"):
  with members:
    - "use what you have video retrieval using representations from collaborative experts"
    - "language conditioned graph networks for relational reasoning"
    - "simple bert models for relation extraction and semantic role labeling"
    - "evaluating text to image matching using binary image selection bison"
    - "relation aware graph attention network for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"
    - "a joint sequence fusion model for video question answering and retrieval"

Connected through hyperedge HE-4 (induced by source paper "imram iterative matching with recurrent attention memory for cross modal image text retrieval"):
  with members:
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "saliency guided attention network for image sentence matching"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "stacked cross attention for image text matching"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "webly supervised joint embedding for cross modal image text retrieval"
    - "multi task learning of hierarchical vision language representation"

Connected through hyperedge HE-5 (induced by source paper "mutatt visual textual mutual guidance for referring expression comprehension"):
  with members:
    - "learning to collocate neural modules for image captioning"
    - "dynamic graph attention for referring expression comprehension"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "visual reasoning with multi hop feature modulation"
    - "identity aware textual visual matching with latent co attention"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"

Connected through hyperedge HE-6 (induced by source paper "target oriented deformation of visual semantic embedding space"):
  with members:
    - "camp cross modal adaptive message passing for text image retrieval"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "matching images and text with multi modal tensor fusion and re ranking"
    - "learning similarity conditions without explicit supervision"
    - "sodeep a sorting deep net to learn ranking loss surrogates"
    - "focus your attention a bidirectional focal attention network for image text matching"
    - "learning visual relation priors for image text matching and image captioning with neural scene graph generators"
    - "finding beans in burgers deep semantic visual embedding with localization"

Connected through hyperedge HE-7 (induced by source paper "rethinking zero shot learning a conditional visual classification perspective"):
  with members:
    - "learning feature to feature translator by alternating back propagation for generative zero shot learning"
    - "residual non local attention networks for image restoration"
    - "multimodal style transfer via graph cuts"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "selective zero shot classification with augmented attributes"
    - "discriminative learning of latent features for zero shot recognition"
    - "support neighbor loss for person re identification"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"

Connected through hyperedge HE-8 (induced by source paper "multimodal style transfer via graph cuts"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "stereoscopic neural style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the contextual loss for image transformation with non aligned data"
    - "demystifying neural style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"

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)