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: "joining hands exploiting monolingual treebanks for parsing of code mixing data"
  Abstract: In this paper, we propose efficient and less resource-intensive strategies for parsing of code-mixed data. These strategies are not constrained by in-domain annotations, rather they leverage pre-existing monolingual annotated resources for training. We show that these methods can produce significantly better results as compared to an informed baseline. Besides, we also present a data set of 450 Hindi and English code-mixed tweets of Hindi multilingual speakers for evaluation. The data set is manually annotated with Universal Dependencies.

Connected through hyperedge HE-1 (induced by source paper "leveraging pretrained word embeddings for part of speech tagging of code switching data"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "part of speech tagging for code switched data"
    - "creating a large multi layered representational repository of linguistic code switched arabic data"
    - "universal dependency parsing for hindi english code switching"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "stack propagation improved representation learning for syntax"

Connected through hyperedge HE-2 (induced by source paper "english bhojpuri smt system insights from the karaka model"):
  with members:
    - "the rgnlp machine translation systems for wat 2018"
    - "automatic identification of closely related indian languages resources and experiments"
    - "demo of sanskrit hindi smt system"
    - "opennmt open source toolkit for neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "nematus a toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "reordering rules for english hindi smt"

Connected through hyperedge HE-3 (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"
    - "crowdsourcing universal part of speech tags for code switching"
    - "a multi task approach for named entity recognition in social media data"

Connected through hyperedge HE-4 (induced by source paper "small and practical bert models for sequence labeling"):
  with members:
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "82 treebanks 34 models universal dependency parsing with multi treebank models"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "enriching word vectors with subword information"

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)