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: "a generative parser with a discriminative recognition algorithm"
  Abstract: Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised neural semantic parsing with a generative ranker"):
  with members:
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "universal semantic parsing"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "weakly supervised semantic parsing with abstract examples"
    - "learning a neural semantic parser from user feedback"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "learning executable semantic parsers for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "dependency grammar induction with a neural variational transition based parser"):
  with members:
    - "global transition based non projective dependency parsing"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "crf autoencoder for unsupervised dependency parsing"
    - "recurrent neural network grammars"
    - "enriching word vectors with subword information"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "language as a latent variable discrete generative models for sentence compression"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-3 (induced by source paper "learning an executable neural semantic parser"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "universal semantic parsing"
    - "structured attention networks"
    - "learning structured text representations"
    - "learning to paraphrase for question answering"
    - "attention is all you need"
    - "making neural programming architectures generalize via recursion"

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)