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: "dual latent variable model for low resource natural language generation in dialogue systems"
  Abstract: Recent deep learning models have shown improving results to natural language generation (NLG) irrespective of providing sufficient annotated data. However, a modest training data may harm such models performance. Thus, how to build a generator that can utilize as much of knowledge from a low-resource setting data is a crucial issue in NLG. This paper presents a variational neural-based generation model to tackle the NLG problem of having limited labeled dataset, in which we integrate a variational inference into an encoder-decoder generator and introduce a novel auxiliary autoencoding with an effective training procedure. Experiments showed that the proposed methods not only outperform the previous models when having sufficient training dataset but also show strong ability to work acceptably well when the training data is scarce.

Connected through hyperedge HE-1 (induced by source paper "low resource response generation with template prior"):
  with members:
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "text generation with exemplar based adaptive decoding"
    - "universal neural machine translation for extremely low resource languages"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "learning neural templates for text generation"
    - "conversational ai the science behind the alexa prize"
    - "generating sentences by editing prototypes"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-2 (induced by source paper "key fact as pivot a two stage model for low resource table to text generation"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "sgm sequence generation model for multi label classification"
    - "end to end content and plan selection for data to text generation"
    - "learning neural templates for text generation"
    - "phrase based neural unsupervised machine translation"
    - "data to text generation with content selection and planning"
    - "attention is all you need"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "meta learning for low resource natural language generation in task oriented dialogue systems"):
  with members:
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning for low resource neural machine translation"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"

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)