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: "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
  Abstract: Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmentation that yield improved results in our model. We test the model on three datasets in the restaurant, TV and laptop domains, and report both objective and subjective evaluations of our best model. Using a range of automatic metrics, as well as human evaluators, we show that our approach achieves better results than state-of-the-art models on the same datasets.

Connected through hyperedge HE-1 (induced by source paper "stochastic natural language generation using dependency information"):
  with members:
    - "end to end content and plan selection for data to text generation"
    - "findings of the e2e nlg challenge"
    - "natural language generation for spoken dialogue system using rnn encoder decoder networks"
    - "order planning neural text generation from structured data"
    - "the e2e dataset new challenges for end to end generation"
    - "table to text generation by structure aware seq2seq learning"
    - "neural text generation from structured data with application to the biography domain"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"

Connected through hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "dual supervised learning for natural language understanding and generation"
    - "copy mechanism and tailored training for character based data to text generation"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "rankme reliable human ratings for natural language generation"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"

Connected through hyperedge HE-3 (induced by source paper "pragmatically informative text generation"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "generating bilingual pragmatic color references"
    - "pragmatically informative image captioning with character level reference"
    - "findings of the e2e nlg challenge"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "unified pragmatic models for generating and following instructions"
    - "context aware captions from context agnostic supervision"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "constrained decoding for neural nlg from compositional representations in task oriented dialogue"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "findings of the e2e nlg challenge"
    - "data to text generation with content selection and planning"

Connected through hyperedge HE-5 (induced by source paper "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "end to end content and plan selection for data to text generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "neural machine translation with gumbel greedy decoding"
    - "listening while speaking speech chain by deep learning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "maximizing stylistic control and semantic accuracy in nlg personality variation and discourse contrast"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "neural multivoice models for expressing novel personalities in dialog"
    - "learning neural templates for text generation"
    - "toward controlled generation of text"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "natural language generation at scale a case study for open domain question answering"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "findings of the e2e nlg challenge"
    - "iso standard domain independent dialogue act tagging for conversational agents"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-8 (induced by source paper "semantic noise matters for neural natural language generation"):
  with members:
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "can neural generators for dialogue learn sentence planning and discourse structuring"
    - "object hallucination in image captioning"
    - "findings of the e2e nlg challenge"
    - "challenges in data to document generation"
    - "six challenges for neural machine translation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "sequence to sequence generation for spoken dialogue via deep syntax trees and strings"

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)