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: "grammatical constraints on intra sentential code switching from theories to working models"
  Abstract: We make one of the first attempts to build working models for intra-sentential code-switching based on the Equivalence-Constraint (Poplack 1980) and Matrix-Language (Myers-Scotton 1993) theories. We conduct a detailed theoretical analysis, and a small-scale empirical study of the two models for Hindi-English CS. Our analyses show that the models are neither sound nor complete. Taking insights from the errors made by the models, we propose a new model that combines features of both the theories.

Connected through hyperedge HE-1 (induced by source paper "improved sentiment detection via label transfer from monolingual to synthetic code switched text"):
  with members:
    - "a deep generative model for code switched text"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "adversarial evaluation of dialogue models"
    - "towards sub word level compositions for sentiment analysis of hindi english code mixed text"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-2 (induced by source paper "a survey of code switched speech and language processing"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "building a unified code switching asr system for south african languages"
    - "hindi english code switching speech corpus"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "a dataset for building code mixed goal oriented conversation systems"
    - "study of semi supervised approaches to improving english mandarin code switching speech recognition"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "crowdsourcing universal part of speech tags for code switching"

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)