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: "simpler context dependent logical forms via model projections"
  Abstract: We consider the task of learning a context-dependent mapping from utterances to denotations. With only denotations at training time, we must search over a combinatorially large space of logical forms, which is even larger with context-dependent utterances. To cope with this challenge, we perform successive projections of the full model onto simpler models that operate over equivalence classes of logical forms. Though less expressive, we find that these simpler models are much faster and can be surprisingly effective. Moreover, they can be used to bootstrap the full model. Finally, we collected three new context-dependent semantic parsing datasets, and develop a new left-to-right parser.

Connected through hyperedge HE-1 (induced by source paper "how far are we from effective context modeling an exploratory study on semantic parsing in context"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"
    - "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"
    - "learning to map context dependent sentences to executable formal queries"
    - "mapping language to code in programmatic context"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-2 (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-3 (induced by source paper "multi task learning for conversational question answering over a large scale knowledge base"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "coarse to fine decoding for neural semantic parsing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"
    - "a syntactic neural model for general purpose code generation"

Connected through hyperedge HE-4 (induced by source paper "coupling retrieval and meta learning for context dependent semantic parsing"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "dictionary guided editing networks for paraphrase generation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "response generation by context aware prototype editing"
    - "mapping language to code in programmatic context"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-5 (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-6 (induced by source paper "spoc search based pseudocode to code"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "frangel component based synthesis with control structures"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "naps natural program synthesis dataset"
    - "mapping language to code in programmatic context"
    - "coarse to fine decoding for neural semantic parsing"
    - "retrieval based neural code generation"

Connected through hyperedge HE-7 (induced by source paper "juice a large scale distantly supervised dataset for open domain context based code generation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "mapping language to code in programmatic context"
    - "learning to mine aligned code and natural language pairs from stack overflow"
    - "attention is all you need"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-8 (induced by source paper "effective search of logical forms for weakly supervised knowledge based question answering"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "coarse to fine decoding for neural semantic parsing"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "attention is all you need"
    - "macro grammars and holistic triggering for efficient semantic parsing"
    - "inferring logical forms from denotations"

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)