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: "grounded semantic composition for visual scenes"
  Abstract: We present a visually-grounded language understanding model based on a study of how people verbally describe objects in scenes. The emphasis of the model is on the combination of individual word meanings to produce meanings for complex referring expressions. The model has been implemented, and it is able to understand a broad range of spatial referring expressions. We describe our implementation of word level visually-grounded semantics and their embedding in a compositional parsing framework. The implemented system selects the correct referents in response to natural language expressions for a large percentage of test cases. In an analysis of the system's successes and failures we reveal how visual context influences the semantics of utterances and propose future extensions to the model that take such context into account.

Connected through hyperedge HE-1 (induced by source paper "referring to the recently seen reference and perceptual memory in situated dialog"):
  with members:
    - "using regular languages to explore the representational capacity of recurrent neural architectures"
    - "the symbol grounding problem"

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"
    - "do you see what i mean visual resolution of linguistic ambiguities"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "what is not where the challenge of integrating spatial representations into deep learning architectures"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "learning to compose neural networks for question answering"
    - "deep learning in neural networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "sequence to sequence learning with neural networks"
    - "the symbol grounding problem"

Connected through hyperedge HE-4 (induced by source paper "show me the cup reference with continuous representations"):
  with members:
    - "generation and comprehension of unambiguous object descriptions"
    - "exploring models and data for image question answering"
    - "show attend and tell neural image caption generation with visual attention"
    - "inferring algorithmic patterns with stack augmented recurrent nets"
    - "very deep convolutional networks for large scale image recognition"
    - "neural machine translation by jointly learning to align and translate"
    - "a multi world approach to question answering about real world scenes based on uncertain input"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-5 (induced by source paper "text to 3d scene generation with rich lexical grounding"):
  with members:
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-6 (induced by source paper "context based word acquisition for situated dialogue in a virtual world"):
  with members:
    - "grounding the lexical semantics of verbs in visual perception using force dynamics and event logic"

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.AI(Artificial Intelligence)