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: "there s no comparison reference less evaluation metrics in grammatical error correction"
  Abstract: Current methods for automatically evaluating grammatical error correction (GEC) systems rely on gold-standard references. However, these methods suffer from penalizing grammatical edits that are correct but not in the gold standard. We show that reference-less grammaticality metrics correlate very strongly with human judgments and are competitive with the leading reference-based evaluation metrics. By interpolating both methods, we achieve state-of-the-art correlation with human judgments. Finally, we show that GEC metrics are much more reliable when they are calculated at the sentence level instead of the corpus level. We have set up a CodaLab site for benchmarking GEC output using a common dataset and different evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "towards minimal supervision bert based grammar error correction"):
  with members:
    - "a neural grammatical error correction system built on better pre training and sequential transfer learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-2 (induced by source paper "diamonds in the rough generating fluent sentences from early stage drafts for academic writing assistance"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "corpora generation for grammatical error correction"
    - "bertscore evaluating text generation with bert"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "text infilling"
    - "style transfer through back translation"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "content preserving text generation with attribute controls"

Connected through hyperedge HE-3 (induced by source paper "automatic quality estimation for natural language generation ranting jointly rating and ranking"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "reference less quality estimation of text simplification systems"
    - "end to end content and plan selection for data to text generation"
    - "a practical approach to dialogue response generation in closed domains"
    - "referenceless quality estimation for natural language generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"
    - "challenges in data to document generation"

Connected through hyperedge HE-4 (induced by source paper "an analysis of source side grammatical errors in nmt"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "reference less quality estimation of text simplification systems"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "neural machine translation of text from non native speakers"
    - "understanding back translation at scale"

Connected through hyperedge HE-5 (induced by source paper "on conducting better validation studies of automatic metrics in natural language generation evaluation"):
  with members:
    - "deep contextualized word representations"
    - "a call for clarity in reporting bleu scores"
    - "evaluation of automatic video captioning using direct assessment"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "referenceless quality estimation for natural language generation"

Connected through hyperedge HE-6 (induced by source paper "reaching human level performance in automatic grammatical error correction an empirical study"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "inherent biases in reference based evaluation for grammatical error correction and text simplification"
    - "joint training for neural machine translation models with monolingual data"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "attention is all you need"
    - "adapting sequence models for sentence correction"

Connected through hyperedge HE-7 (induced by source paper "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"):
  with members:
    - "toward controlled generation of text"
    - "opennmt open source toolkit for neural machine translation"
    - "controlling linguistic style aspects in neural language generation"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "edinburgh neural machine translation systems for wmt 16"
    - "improving neural machine translation models with monolingual data"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "inherent biases in reference based evaluation for grammatical error correction and text simplification"):
  with members:
    - "automatic metric validation for grammatical error correction"
    - "sentence simplification with deep reinforcement learning"
    - "phrase based machine translation is state of the art for automatic grammatical error correction"
    - "neural language correction with character based attention"
    - "unsupervised sentence simplification using deep semantics"
    - "more accurate tests for the statistical significance of result differences"

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)