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: "unified pragmatic models for generating and following instructions"
  Abstract: We extend models for both following and generating natural language instructions by adding an explicit pragmatic layer. These pragmatics-enabled models explicitly reason about why speakers produce certain instructions, and about how listeners will react upon hearing them. Given learned base listener and speaker models, we build a pragmatic listener that uses the base speaker to reason counterfactually about alternative action descriptions, and a pragmatic speaker that uses the base listener to simulate the interpretation of candidate instruction sequences. Evaluation of language generation and interpretation in the SAIL navigation and SCONE instruction following datasets shows that the pragmatic inference procedure improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at producing instructions correctly interpretable by humans) in diverse settings.

Connected through hyperedge HE-1 (induced by source paper "editing based sql query generation for cross domain context dependent questions"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "recursive and clause wise decoding for complex and cross domain text to sql generation"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "sparc cross domain semantic parsing in context"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "question generation from sql queries improves neural semantic parsing"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "executing instructions in situated collaborative interactions"):
  with members:
    - "meetup a corpus of joint activity dialogues in a visual environment"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "pragmatically informative text generation"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "generating bilingual pragmatic color references"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "pragmatically informative image captioning with character level reference"
    - "findings of the e2e nlg challenge"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "context aware captions from context agnostic supervision"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "hierarchical decision making by generating and following natural language instructions"):
  with members:
    - "relational deep reinforcement learning"
    - "speaker follower models for vision and language navigation"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "starcraft ii a new challenge for reinforcement learning"
    - "parlai a dialog research software platform"
    - "zero shot task generalization with multi task deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "are you looking grounding to multiple modalities in vision and language navigation"):
  with members:
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "speaker follower models for vision and language navigation"
    - "blindfold baselines for embodied qa"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "visual semantic navigation using scene priors"
    - "shifting the baseline single modality performance on visual navigation qa"

Connected through hyperedge HE-6 (induced by source paper "building an application independent natural language interface"):
  with members:
    - "spatialnli a spatial domain natural language interface to databases using spatial comprehension"
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "transferable natural language interface to structured queries aided by adversarial generation"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "an end to end neural natural language interface for databases"
    - "explain yourself a natural language interface for scrutable autonomous robots"
    - "mapping natural language commands to web elements"

Connected through hyperedge HE-7 (induced by source paper "flowdelta modeling flow information gain in reasoning for conversational machine comprehension"):
  with members:
    - "attentive history selection for conversational question answering"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "run through the streets a new dataset and baseline models for realistic urban navigation"):
  with members:
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "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"
    - "chalet cornell house agent learning environment"
    - "vision based navigation with language based assistance via imitation learning with indirect intervention"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"

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)