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: "can neural generators for dialogue learn sentence planning and discourse structuring"
  Abstract: Responses in task-oriented dialogue systems often realize multiple propositions whose ultimate form depends on the use of sentence planning and discourse structuring operations. For example a recommendation may consist of an explicitly evaluative utterance e.g. Chanpen Thai is the best option, along with content related by the justification discourse relation, e.g. It has great food and service, that combines multiple propositions into a single phrase. While neural generation methods integrate sentence planning and surface realization in one end-to-end learning framework, previous work has not shown that neural generators can: (1) perform common sentence planning and discourse structuring operations; (2) make decisions as to whether to realize content in a single sentence or over multiple sentences; (3) generalize sentence planning and discourse relation operations beyond what was seen in training. We systematically create large training corpora that exhibit particular sentence planning operations and then test neural models to see what they learn. We compare models without explicit latent variables for sentence planning with ones that provide explicit supervision during training. We show that only the models with additional supervision can reproduce sentence planing and discourse operations and generalize to situations unseen in training.

Connected through hyperedge HE-1 (induced by source paper "variational template machine for data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "using monolingual data in neural machine translation a systematic study"
    - "the curious case of neural text degeneration"
    - "table to text generation with effective hierarchical encoder on three dimensions row column and time"
    - "generating sentences from disentangled syntactic and semantic spaces"
    - "disentangled representation learning for non parallel text style transfer"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "describing a knowledge base"

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"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"

Connected through hyperedge HE-3 (induced by source paper "curate and generate a corpus and method for joint control of semantics and style in neural nlg"):
  with members:
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "opennmt neural machine translation toolkit"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "characterizing variation in crowd sourced data for training neural language generators to produce stylistically varied outputs"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "toward controlled generation of text"

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"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "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 "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"
    - "neural multivoice models for expressing novel personalities in dialog"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "learning neural templates for text generation"
    - "toward controlled generation of text"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-6 (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"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "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)