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: "the photobook dataset building common ground through visually grounded dialogue"
  Abstract: This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation. Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions. We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected. To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain. Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction.

Connected through hyperedge HE-1 (induced by source paper "meetup a corpus of joint activity dialogues in a visual environment"):
  with members:
    - "learning to speak and act in a fantasy text adventure game"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "textworld a learning environment for text based games"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "embodied question answering"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "guesswhat visual object discovery through multi modal dialogue"

Connected through hyperedge HE-2 (induced by source paper "an annotated corpus of reference resolution for interpreting common grounding"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "a natural language corpus of common grounding under continuous and partially observable context"
    - "annotation artifacts in natural language inference data"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "what makes reading comprehension questions easier"
    - "analysis methods in neural language processing a survey"

Connected through hyperedge HE-3 (induced by source paper "grounded agreement games emphasizing conversational grounding in visual dialogue settings"):
  with members:
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "natural language semantics with pictures some language vision datasets and potential uses for computational semantics"
    - "a natural language corpus of common grounding under continuous and partially observable context"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "vqa visual question answering"

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)