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: "meetup a corpus of joint activity dialogues in a visual environment"
  Abstract: Building computer systems that can converse about their visual environment is one of the oldest concerns of research in Artificial Intelligence and Computational Linguistics (see, for example, Winograd's 1972 SHRDLU system). Only recently, however, have methods from computer vision and natural language processing become powerful enough to make this vision seem more attainable. Pushed especially by developments in computer vision, many data sets and collection environments have recently been published that bring together verbal interaction and visual processing. Here, we argue that these datasets tend to oversimplify the dialogue part, and we propose a task---MeetUp!---that requires both visual and conversational grounding, and that makes stronger demands on representations of the discourse. MeetUp! is a two-player coordination game where players move in a visual environment, with the objective of finding each other. To do so, they must talk about what they see, and achieve mutual understanding. We describe a data collection and show that the resulting dialogues indeed exhibit the dialogue phenomena of interest, while also challenging the language & vision aspect.

Connected through hyperedge HE-1 (induced by source paper "crwiz a framework for crowdsourcing real time wizard of oz dialogues"):
  with members:
    - "natural language interaction to facilitate mental models of remote robots"
    - "persuasion for good towards a personalized persuasive dialogue system for social good"
    - "end to end trainable non collaborative dialog system"
    - "the orca hub explainable offshore robotics through intelligent interfaces"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "edina building an open domain socialbot with self dialogues"

Connected through hyperedge HE-2 (induced by source paper "executing instructions in situated collaborative interactions"):
  with members:
    - "hierarchical decision making by generating and following natural language instructions"
    - "a natural language corpus of common grounding under continuous and partially observable context"
    - "learning to map context dependent sentences to executable formal queries"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "decoupling strategy and generation in negotiation dialogues"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"

Connected through hyperedge HE-3 (induced by source paper "aloha artificial learning of human attributes for dialogue agents"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "bert for coreference resolution baselines and analysis"
    - "learning to speak and act in a fantasy text adventure game"
    - "keep calm and switch on preserving sentiment and fluency in semantic text exchange"
    - "game based video context dialogue"
    - "alquist the alexa prize socialbot"

Connected through hyperedge HE-4 (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"
    - "the photobook dataset building common ground through visually grounded dialogue"
    - "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-5 (induced by source paper "grounded agreement games emphasizing conversational grounding in visual dialogue settings"):
  with members:
    - "the photobook dataset building common ground through visually grounded dialogue"
    - "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)