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 correction as a foreign language"
  Abstract: In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework. This reformulation enabled us to use a single model for solving the problem that is traditionally formulated as learning a language model and an error model. This model employs multi-layer recurrent neural networks as an encoder and a decoder. We demonstrate the effectiveness of this model using an internal dataset, where the training data is automatically obtained from user logs. The model offers competitive performance as compared to the state of the art methods but does not require any feature engineering nor hand tuning between models.

Connected through hyperedge HE-1 (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"
    - "spelling error correction using a nested rnn model and pseudo training data"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "grammatical error correction with neural reinforcement learning"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "report on the sigir 2019 workshop on ecommerce ecom19"):
  with members:
    - "an empirical comparison of faiss and fenshses for nearest neighbor search in hamming space"
    - "production ranking systems a review"
    - "personalized query auto completion through a lightweight representation of the user context"
    - "ranking sentences from product description bullets for better search"
    - "leverage implicit feedback for context aware product search"

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)