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: "abstract syntax networks for code generation and semantic parsing"
  Abstract: Tasks like code generation and semantic parsing require mapping unstructured (or partially structured) inputs to well-formed, executable outputs. We introduce abstract syntax networks, a modeling framework for these problems. The outputs are represented as abstract syntax trees (ASTs) and constructed by a decoder with a dynamically-determined modular structure paralleling the structure of the output tree. On the benchmark Hearthstone dataset for code generation, our model obtains 79.2 BLEU and 22.7% exact match accuracy, compared to previous state-of-the-art values of 67.1 and 6.1%. Furthermore, we perform competitively on the Atis, Jobs, and Geo semantic parsing datasets with no task-specific engineering.

Connected through hyperedge HE-1 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model"):
  with members:
    - "genie a generator of natural language semantic parsers for virtual assistant commands"
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"

Connected through hyperedge HE-2 (induced by source paper "a framework for end to end learning on semantic tree structured data"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "deep sets"
    - "learning to play general video games via an object embedding network"
    - "tree to tree neural networks for program translation"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "structural attention neural networks for improved sentiment analysis"
    - "proximal policy optimization algorithms"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "convo what does conversational programming need an exploration of machine learning interface design"):
  with members:
    - "a comparison of online automatic speech recognition systems and the nonverbal responses to unintelligible speech"
    - "pumice a multi modal agent that learns concepts and conditionals from natural language and demonstrations"
    - "program synthesis and semantic parsing with learned code idioms"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "meet cyrus the query by voice mobile assistant for the tutoring and formative assessment of sql learners"
    - "attention based models for speech recognition"

Connected through hyperedge HE-4 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-5 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs"):
  with members:
    - "learning to infer program sketches"
    - "learning to generalize from sparse and underspecified rewards"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "semantic parsing with syntax and table aware sql generation"
    - "allennlp a deep semantic natural language processing platform"
    - "decoupling structure and lexicon for zero shot semantic parsing"

Connected through hyperedge HE-6 (induced by source paper "structural language models for any code generation"):
  with members:
    - "structural language models of code"
    - "learning neurosymbolic generative models via program synthesis"
    - "learning to infer program sketches"
    - "learning programmatic idioms for scalable semantic parsing"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "write execute assess program synthesis with a repl"
    - "structured neural summarization"
    - "open vocabulary learning on source code with a graph structured cache"

Connected through hyperedge HE-7 (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-8 (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"

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)