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: "probabilistic natural language generation with wasserstein autoencoders"
  Abstract: Probabilistic generation of natural language sentences is an important task in NLP. Existing models such as variational autoencoders (VAE) for sequence generation are extremely difficult to train due to the issues associated with the Kullback-Leibler (KL) loss collapsing to zero. One has to implement various heuristics such as KL weight annealing and word dropout in a carefully engineered manner to successfully train a text VAE. In this paper, we propose the use of Wasserstein autoencoders (WAE) for probabilistic natural language sentence generation. We show that sequence-to-sequence WAEs are more robust towards hyperparameters and can be trained in a straightforward manner without the need for any weight annealing. Empirical evidence shows that the latent space learned by WAEs exhibits properties of continuity and smoothness as in VAEs, while simultaneously achieving much higher BLEU scores for sentence reconstruction.

Connected through hyperedge HE-1 (induced by source paper "conditional response generation using variational alignment"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "a hierarchical latent structure for variational conversation modeling"
    - "improving variational encoder decoders in dialogue generation"
    - "style transfer from non parallel text by cross alignment"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "adversarial learning for neural dialogue generation"
    - "variational attention for sequence to sequence models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "natural language generation with neural variational models"):
  with members:
    - "learning to ask neural question generation for reading comprehension"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "latent variable dialogue models and their diversity"
    - "adversarial learning for neural dialogue generation"
    - "variational attention for sequence to sequence models"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to sequence learning as beam search optimization"

Connected through hyperedge HE-3 (induced by source paper "disentangled representation learning for non parallel text style transfer"):
  with members:
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "deep photo style transfer"
    - "style transfer in text exploration and evaluation"
    - "variational attention for sequence to sequence models"
    - "disentangling factors of variation in deep representations using adversarial training"

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)