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: "structuring latent spaces for stylized response generation"
  Abstract: Generating responses in a targeted style is a useful yet challenging task, especially in the absence of parallel data. With limited data, existing methods tend to generate responses that are either less stylized or less context-relevant. We propose StyleFusion, which bridges conversation modeling and non-parallel style transfer by sharing a structured latent space. This structure allows the system to generate stylized relevant responses by sampling in the neighborhood of the conversation model prediction, and continuously control the style level. We demonstrate this method using dialogues from Reddit data and two sets of sentences with distinct styles (arXiv and Sherlock Holmes novels). Automatic and human evaluation show that, without sacrificing appropriateness, the system generates responses of the targeted style and outperforms competitive baselines.

Connected through hyperedge HE-1 (induced by source paper "dialogpt large scale generative pre training for conversational response generation"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "dlgnet a transformer based model for dialogue response generation"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "consistent dialogue generation with self supervised feature learning"
    - "investigating evaluation of open domain dialogue systems with human generated multiple references"

Connected through hyperedge HE-2 (induced by source paper "stylized text generation using wasserstein autoencoders with a mixture of gaussian prior"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "topic guided variational autoencoders for text generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "disentangled representation learning for non parallel text style transfer"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-3 (induced by source paper "zero shot fine grained style transfer leveraging distributed continuous style representations to transfer to unseen styles"):
  with members:
    - "controllable unsupervised text attribute transfer via editing entangled latent representation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "revision in continuous space unsupervised text style transfer without adversarial learning"
    - "build it break it fix it for dialogue safety robustness from adversarial human attack"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "style transfer as unsupervised machine translation"
    - "style transfer through back translation"
    - "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)