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: "treegan syntax aware sequence generation with generative adversarial networks"
  Abstract: Generative Adversarial Networks (GANs) have shown great capacity on image generation, in which a discriminative model guides the training of a generative model to construct images that resemble real images. Recently, GANs have been extended from generating images to generating sequences (e.g., poems, music and codes). Existing GANs on sequence generation mainly focus on general sequences, which are grammar-free. In many real-world applications, however, we need to generate sequences in a formal language with the constraint of its corresponding grammar. For example, to test the performance of a database, one may want to generate a collection of SQL queries, which are not only similar to the queries of real users, but also follow the SQL syntax of the target database. Generating such sequences is highly challenging because both the generator and discriminator of GANs need to consider the structure of the sequences and the given grammar in the formal language. To address these issues, we study the problem of syntax-aware sequence generation with GANs, in which a collection of real sequences and a set of pre-defined grammatical rules are given to both discriminator and generator. We propose a novel GAN framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG) into the sequence generation process. In TreeGAN, the generator employs a recurrent neural network (RNN) to construct a parse tree. Each generated parse tree can then be translated to a valid sequence of the given grammar. The discriminator uses a tree-structured RNN to distinguish the generated trees from real trees. We show that TreeGAN can generate sequences for any CFG and its generation fully conforms with the given syntax. Experiments on synthetic and real data sets demonstrated that TreeGAN significantly improves the quality of the sequence generation in context-free languages.

Connected through hyperedge HE-1 (induced by source paper "text modeling with syntax aware variational autoencoders"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "variational knowledge graph reasoning"
    - "syntax directed variational autoencoder for structured data"
    - "dirichlet variational autoencoder for text modeling"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-2 (induced by source paper "gancoder an automatic natural language to programming language translation approach based on gan"):
  with members:
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "egocoder intelligent program synthesis with hierarchical sequential neural network model"
    - "a survey on semantic parsing"
    - "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"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "latent predictor networks for code generation"

Connected through hyperedge HE-3 (induced by source paper "attention models in graphs a survey"):
  with members:
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "recurrent neural network attention mechanisms for interpretable system log anomaly detection"
    - "spine structural identity preserved inductive network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "on sampling from massive graph streams"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"

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)