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: "shallow parsing pipeline for hindi english code mixed social media text"
  Abstract: In this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed. We have annotated the data, developed a language identifier, a normalizer, a part-of-speech tagger and a shallow parser. To the best of our knowledge, we are the first to attempt shallow parsing on CSMT. The pipeline developed has been made available to the research community with the goal of enabling better text analysis of Hindi English CSMT. The pipeline is accessible at 1.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning strategies for hindi english codemixed sentiment analysis"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "an ensemble model for sentiment analysis of hindi english code mixed data"
    - "universal language model fine tuning for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "towards sub word level compositions for sentiment analysis of hindi english code mixed text"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"):
  with members:
    - "analyzing roles of classifiers and code mixed factors for sentiment identification"
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "automatic normalization of word variations in code mixed social media text"
    - "preparing bengali english code mixed corpus for sentiment analysis of indian languages"
    - "attention is all you need"
    - "deepnorm a deep learning approach to text normalization"
    - "fully character level neural machine translation without explicit segmentation"
    - "rnn approaches to text normalization a challenge"

Connected through hyperedge HE-3 (induced by source paper "joining hands exploiting monolingual treebanks for parsing of code mixing data"):
  with members:
    - "challenges of computational processing of code switching"
    - "many languages one parser"
    - "natural language processing almost from scratch"

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)