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: "generating sentences from disentangled syntactic and semantic spaces"
  Abstract: Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous latent space does not explicitly model the syntactic information. In this paper, we propose to generate sentences from disentangled syntactic and semantic spaces. Our proposed method explicitly models syntactic information in the VAE's latent space by using the linearized tree sequence, leading to better performance of language generation. Additionally, the advantage of sampling in the disentangled syntactic and semantic latent spaces enables us to perform novel applications, such as the unsupervised paraphrase generation and syntax-transfer generation. Experimental results show that our proposed model achieves similar or better performance in various tasks, compared with state-of-the-art related work.

Connected through hyperedge HE-1 (induced by source paper "variational template machine for data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "using monolingual data in neural machine translation a systematic study"
    - "the curious case of neural text degeneration"
    - "table to text generation with effective hierarchical encoder on three dimensions row column and time"
    - "disentangled representation learning for non parallel text style transfer"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "describing a knowledge base"

Connected through hyperedge HE-2 (induced by source paper "on variational learning of controllable representations for text without supervision"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "preventing posterior collapse with delta vaes"
    - "transforming delete retrieve generate approach for controlled text style transfer"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "content preserving text generation with attribute controls"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"

Connected through hyperedge HE-3 (induced by source paper "leveraging structural and semantic correspondence for attribute oriented aspect sentiment discovery"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "disentangled representation learning for non parallel text style transfer"
    - "improving topic models with latent feature word representations"
    - "learning disentangled representations of texts with application to biomedical abstracts"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "attention is all you need"
    - "distributed representations of words and phrases and their compositionality"
    - "the author topic model for authors and documents"

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)