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: "wronging a right generating better errors to improve grammatical error detection"
  Abstract: Grammatical error correction, like other machine learning tasks, greatly benefits from large quantities of high quality training data, which is typically expensive to produce. While writing a program to automatically generate realistic grammatical errors would be difficult, one could learn the distribution of naturallyoccurring errors and attempt to introduce them into other datasets. Initial work on inducing errors in this way using statistical machine translation has shown promise; we investigate cheaply constructing synthetic samples, given a small corpus of human-annotated data, using an off-the-rack attentive sequence-to-sequence model and a straight-forward post-processing procedure. Our approach yields error-filled artificial data that helps a vanilla bi-directional LSTM to outperform the previous state of the art at grammatical error detection, and a previously introduced model to gain further improvements of over 5% $F_{0.5}$ score. When attempting to determine if a given sentence is synthetic, a human annotator at best achieves 39.39 $F_1$ score, indicating that our model generates mostly human-like instances.

Connected through hyperedge HE-1 (induced by source paper "automatic compilation of resources for academic writing and evaluating with informal word identification and paraphrasing system"):
  with members:
    - "tf ranking scalable tensorflow library for learning to rank"
    - "a report on the complex word identification shared task 2018"
    - "par4sim adaptive paraphrasing for text simplification"
    - "simple unsupervised keyphrase extraction using sentence embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic multi level multi task learning for sentence simplification"
    - "learning to paraphrase for question answering"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "encode tag realize high precision text editing"):
  with members:
    - "fine tune bert for extractive summarization"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "imat unsupervised text attribute transfer via iterative matching and translation"
    - "step by step separating planning from realization in neural data to text generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "learning neural templates for text generation"

Connected through hyperedge HE-3 (induced by source paper "the cued s grammatical error correction systems for bea 2019"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "neural grammatical error correction with finite state transducers"
    - "investigating backtranslation in neural machine translation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "the university of cambridge s machine translation systems for wmt18"
    - "why not be versatile applications of the sgnmt decoder for machine translation"
    - "understanding back translation at scale"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "context is key grammatical error detection with contextual word representations"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "auxiliary objectives for neural error detection models"
    - "artificial error generation with machine translation and syntactic patterns"

Connected through hyperedge HE-5 (induced by source paper "the unbearable weight of generating artificial errors for grammatical error correction"):
  with members:
    - "investigating backtranslation in neural machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "attention is all you need"
    - "artificial error generation with machine translation and syntactic patterns"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "neural language correction with character based attention"

Connected through hyperedge HE-6 (induced by source paper "neural semi markov conditional random fields for robust character based part of speech tagging"):
  with members:
    - "character based joint segmentation and pos tagging for chinese using bidirectional rnn crf"
    - "mimicking word embeddings using subword rnns"
    - "a general purpose tagger with convolutional neural networks"
    - "empower sequence labeling with task aware neural language model"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "neural word segmentation learning for chinese"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "robsut wrod reocginiton via semi character recurrent neural network"

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)