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: "spelling error correction using a nested rnn model and pseudo training data"
  Abstract: We propose a nested recurrent neural network (nested RNN) model for English spelling error correction and generate pseudo data based on phonetic similarity to train it. The model fuses orthographic information and context as a whole and is trained in an end-to-end fashion. This avoids feature engineering and does not rely on a noisy channel model as in traditional methods. Experiments show that the proposed method is superior to existing systems in correcting spelling errors.

Connected through hyperedge HE-1 (induced by source paper "combating adversarial misspellings with robust word recognition"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "synthetic and natural noise both break neural machine translation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generating natural adversarial examples"
    - "sentence level grammatical error identification as sequence to sequence correction"

Connected through hyperedge HE-2 (induced by source paper "github typo corpus a large scale multilingual dataset of misspellings and grammatical errors"):
  with members:
    - "misspelling oblivious word embeddings"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "experiments with universal cefr classification"
    - "an analysis of neural language modeling at multiple scales"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "spelling correction as a foreign language"
    - "grammatical error correction with neural reinforcement learning"
    - "language modeling with gated convolutional networks"

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)