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: "do you see what i mean visual resolution of linguistic ambiguities"
  Abstract: Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.

Connected through hyperedge HE-1 (induced by source paper "referring to objects in videos using spatio temporal identifying descriptions"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "visual referring expression recognition what do systems actually learn"
    - "tvqa localized compositional video question answering"
    - "actor and action video segmentation from a sentence"
    - "flow guided feature aggregation for video object detection"
    - "obj2text generating visually descriptive language from object layouts"
    - "dense captioning events in videos"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

Connected through hyperedge HE-2 (induced by source paper "using syntax to ground referring expressions in natural images"):
  with members:
    - "modeling relationships in referential expressions with compositional modular networks"
    - "grounding of textual phrases in images by reconstruction"
    - "learning to compose neural networks for question answering"
    - "resolving language and vision ambiguities together joint segmentation prepositional attachment resolution in captioned scenes"
    - "modeling context between objects for referring expression understanding"
    - "modeling context in referring expressions"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "generation and comprehension of unambiguous object descriptions"

Connected through hyperedge HE-3 (induced by source paper "resolving language and vision ambiguities together joint segmentation prepositional attachment resolution in captioned scenes"):
  with members:
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "vqa visual question answering"
    - "visual madlibs fill in the blank image generation and question answering"
    - "ask your neurons a neural based approach to answering questions about images"
    - "cider consensus based image description evaluation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-4 (induced by source paper "virtual embodiment a scalable long term strategy for artificial intelligence research"):
  with members:
    - "learning language games through interaction"
    - "a roadmap towards machine intelligence"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "mazebase a sandbox for learning from games"
    - "the symbol grounding problem"

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)