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: "learning to split and rephrase from wikipedia edit history"
  Abstract: Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia's edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.

Connected through hyperedge HE-1 (induced by source paper "fact aware sentence split and rephrase with permutation invariant training"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "split and rephrase better evaluation and a stronger baseline"
    - "deep attractor network for single microphone speaker separation"
    - "split and rephrase"
    - "attention is all you need"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural machine translation of rare words with subword units"

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 "corpora generation for grammatical error correction"):
  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"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "attention is all you need"
    - "artificial error generation with machine translation and syntactic patterns"

Connected through hyperedge HE-4 (induced by source paper "transforming complex sentences into a semantic hierarchy"):
  with members:
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "bleu is not suitable for the evaluation of text simplification"
    - "simple and effective text simplification using semantic and neural methods"
    - "split and rephrase"
    - "sentence simplification with deep reinforcement learning"
    - "get to the point summarization with pointer generator networks"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-5 (induced by source paper "discofuse a large scale dataset for discourse based sentence fusion"):
  with members:
    - "training tips for the transformer model"
    - "split and rephrase"
    - "attention is all you need"
    - "automatic prediction of discourse connectives"
    - "implicit discourse relation classification via multi task neural networks"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural machine translation of rare words with subword units"
    - "abstractive multi document summarization via phrase selection and merging"

Connected through hyperedge HE-6 (induced by source paper "minwikisplit a sentence splitting corpus with minimal propositions"):
  with members:
    - "transforming complex sentences into a semantic hierarchy"
    - "semantic structural evaluation for text simplification"
    - "graphene semantically linked propositions in open information extraction"
    - "bleu is not suitable for the evaluation of text simplification"
    - "neural network acceptability judgments"
    - "split and rephrase"

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)