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: "text generation with exemplar based adaptive decoding"
  Abstract: We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e., what to say), and the template influences how to say it. Building on the successful encoder-decoder paradigm, it first encodes the content representation from the given input text; to produce the output, it retrieves exemplar text from the training data as "soft templates," which are then used to construct an exemplar-specific decoder. We evaluate the proposed model on abstractive text summarization and data-to-text generation. Empirical results show that this model achieves strong performance and outperforms comparable baselines.

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"
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "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 "controllable paraphrase generation with a syntactic exemplar"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "toward unsupervised text content manipulation"
    - "smaller text classifiers with discriminative cluster embeddings"
    - "variational sequential labelers for semi supervised learning"
    - "towards generating long and coherent text with multi level latent variable models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "disentangled representation learning for non parallel text style transfer"
    - "adversarial example generation with syntactically controlled paraphrase networks"

Connected through hyperedge HE-3 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "unsupervised recurrent neural network grammars"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"
    - "structural supervision improves learning of non local grammatical dependencies"

Connected through hyperedge HE-4 (induced by source paper "sticking to the facts confident decoding for faithful data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "data to text generation with entity modeling"
    - "handling divergent reference texts when evaluating table to text generation"
    - "calibration entropy rates and memory in language models"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "bottom up abstractive summarization"
    - "learning neural templates for text generation"

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)