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: "automatic annotation of structured facts in images"
  Abstract: Motivated by the application of fact-level image understanding, we present an automatic method for data collection of structured visual facts from images with captions. Example structured facts include attributed objects (e.g.,  ), actions (e.g.,  ), interactions (e.g.,  ), and positional information (e.g.,  ). The collected annotations are in the form of fact-image pairs (e.g.,  and an image region containing this fact). With a language approach, the proposed method is able to collect hundreds of thousands of visual fact annotations with accuracy of 83% according to human judgment. Our method automatically collected more than 380,000 visual fact annotations and more than 110,000 unique visual facts from images with captions and localized them in images in less than one day of processing time on standard CPU platforms.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning based image captioning with embedding reward"):
  with members:
    - "image captioning with semantic attention"
    - "face detection with the faster r cnn"
    - "ssd single shot multibox detector"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "diverse beam search decoding diverse solutions from neural sequence models"
    - "asynchronous methods for deep reinforcement learning"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "dense captioning with joint inference and visual context"

Connected through hyperedge HE-2 (induced by source paper "the promise of premise harnessing question premises in visual question answering"):
  with members:
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "scene graph generation by iterative message passing"
    - "attentive explanations justifying decisions and pointing to the evidence"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "question relevance in vqa identifying non visual and false premise questions"

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)