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: "type driven incremental semantic parsing with polymorphism"
  Abstract: Semantic parsing has made significant progress, but most current semantic parsers are extremely slow (CKY-based) and rather primitive in representation. We introduce three new techniques to tackle these problems. First, we design the first linear-time incremental shift-reduce-style semantic parsing algorithm which is more efficient than conventional cubic-time bottom-up semantic parsers. Second, our parser, being type-driven instead of syntax-driven, uses type-checking to decide the direction of reduction, which eliminates the need for a syntactic grammar such as CCG. Third, to fully exploit the power of type-driven semantic parsing beyond simple types (such as entities and truth values), we borrow from programming language theory the concepts of subtype polymorphism and parametric polymorphism to enrich the type system in order to better guide the parsing. Our system learns very accurate parses in GeoQuery, Jobs and Atis domains.

Connected through hyperedge HE-1 (induced by source paper "semantic parsing with dual learning"):
  with members:
    - "agentgraph towards universal dialogue management with structured deep reinforcement learning"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "question generation from sql queries improves neural semantic parsing"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "dual supervised learning"

Connected through hyperedge HE-2 (induced by source paper "generating logical forms from graph representations of text and entities"):
  with members:
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "self attention with relative position representations"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

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 "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"

Connected through hyperedge HE-5 (induced by source paper "spatialnli a spatial domain natural language interface to databases using spatial comprehension"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "encoding spatial relations from natural language"
    - "improving text to sql evaluation methodology"
    - "a transfer learnable natural language interface for databases"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "dataset for a neural natural language interface for databases nnlidb"
    - "neural semantic parsing over multiple knowledge bases"
    - "learning a neural semantic parser from user feedback"

Connected through hyperedge HE-6 (induced by source paper "exploiting rich syntactic information for semantic parsing with graph to sequence model"):
  with members:
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "word mover s embedding from word2vec to document embedding"
    - "sentence state lstm for text representation"
    - "universal semantic parsing"
    - "synthetic and natural noise both break neural machine translation"
    - "abstract syntax networks for code generation and semantic parsing"
    - "cross domain semantic parsing via paraphrasing"

Connected through hyperedge HE-7 (induced by source paper "a grammar based structural cnn decoder for code generation"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "learning to synthesize"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"
    - "latent predictor networks for code generation"

Connected through hyperedge HE-8 (induced by source paper "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "typesql knowledge based type aware neural text to sql generation"
    - "abstract syntax networks for code generation and semantic parsing"
    - "a syntactic neural model for general purpose code generation"
    - "language to logical form with neural attention"

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)