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: "position focused attention network for image text matching"
  Abstract: Image-text matching tasks have recently attracted a lot of attention in the computer vision field. The key point of this cross-domain problem is how to accurately measure the similarity between the visual and the textual contents, which demands a fine understanding of both modalities. In this paper, we propose a novel position focused attention network (PFAN) to investigate the relation between the visual and the textual views. In this work, we integrate the object position clue to enhance the visual-text joint-embedding learning. We first split the images into blocks, by which we infer the relative position of region in the image. Then, an attention mechanism is proposed to model the relations between the image region and blocks and generate the valuable position feature, which will be further utilized to enhance the region expression and model a more reliable relationship between the visual image and the textual sentence. Experiments on the popular datasets Flickr30K and MS-COCO show the effectiveness of the proposed method. Besides the public datasets, we also conduct experiments on our collected practical large-scale news dataset (Tencent-News) to validate the practical application value of proposed method. As far as we know, this is the first attempt to test the performance on the practical application. Our method achieves the state-of-art performance on all of these three datasets.

Connected through hyperedge HE-1 (induced by source paper "expressing objects just like words recurrent visual embedding for image text matching"):
  with members:
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning semantic concepts and order for image and sentence matching"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "areas of attention for image captioning"
    - "dual attention networks for multimodal reasoning and matching"

Connected through hyperedge HE-2 (induced by source paper "imagebert cross modal pre training with large scale weak supervised image text data"):
  with members:
    - "towards vqa models that can read"
    - "lxmert learning cross modality encoder representations from transformers"
    - "fusion of detected objects in text for visual question answering"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "unified vision language pre training for image captioning and vqa"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-3 (induced by source paper "unicoder vl a universal encoder for vision and language by cross modal pre training"):
  with members:
    - "uniter learning universal image text representations"
    - "fusion of detected objects in text for visual question answering"
    - "unicoder a universal language encoder by pre training with multiple cross lingual tasks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "videobert a joint model for video and language representation learning"
    - "visualbert a simple and performant baseline for vision and language"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"

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.CL(Computation and Language)