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: "reasoning about fine grained attribute phrases using reference games"
  Abstract: We present a framework for learning to describe fine-grained visual differences between instances using attribute phrases. Attribute phrases capture distinguishing aspects of an object (e.g., "propeller on the nose" or "door near the wing" for airplanes) in a compositional manner. Instances within a category can be described by a set of these phrases and collectively they span the space of semantic attributes for a category. We collect a large dataset of such phrases by asking annotators to describe several visual differences between a pair of instances within a category. We then learn to describe and ground these phrases to images in the context of a *reference game* between a speaker and a listener. The goal of a speaker is to describe attributes of an image that allows the listener to correctly identify it within a pair. Data collected in a pairwise manner improves the ability of the speaker to generate, and the ability of the listener to interpret visual descriptions. Moreover, due to the compositionality of attribute phrases, the trained listeners can interpret descriptions not seen during training for image retrieval, and the speakers can generate attribute-based explanations for differences between previously unseen categories. We also show that embedding an image into the semantic space of attribute phrases derived from listeners offers 20% improvement in accuracy over existing attribute-based representations on the FGVC-aircraft dataset.

Connected through hyperedge HE-1 (induced by source paper "tell the difference fine grained visual descriptor via a discriminating referee"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "style transfer from non parallel text by cross alignment"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "detecting visual relationships with deep relational networks"
    - "dense captioning events in videos"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "dynamic computational time for visual attention"

Connected through hyperedge HE-2 (induced by source paper "visual reasoning with multi hop feature modulation"):
  with members:
    - "efficient video object segmentation via network modulation"
    - "mattnet modular attention network for referring expression comprehension"
    - "guide me interacting with deep networks"
    - "modulating early visual processing by language"
    - "visual question answering datasets algorithms and future challenges"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "comprehension guided referring expressions"
    - "parallel attention a unified framework for visual object discovery through dialogs and queries"

Connected through hyperedge HE-3 (induced by source paper "mattnet modular attention network for referring expression comprehension"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "query guided regression network with context policy for phrase grounding"
    - "learning two branch neural networks for image text matching tasks"
    - "an implementation of faster rcnn with study for region sampling"
    - "comprehension guided referring expressions"
    - "learning to reason end to end module networks for visual question answering"
    - "recurrent multimodal interaction for referring image segmentation"
    - "mask r cnn"

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)