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: "multilingual ner transfer for low resource languages"
  Abstract: In massively multilingual transfer NLP models over many source languages are applied to a low-resource target language. In contrast to most prior work, which use a single model or a small handful, we consider many such models, which raises the critical problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer: one based on unsupervised truth inference, and another using limited supervision in the target language. Evaluating on named entity recognition over 41 languages, we show that our techniques are much more effective than strong baselines, including standard ensembling, and our unsupervised method rivals oracle selection of the single best individual model.

Connected through hyperedge HE-1 (induced by source paper "neural cross lingual transfer and limited annotated data for named entity recognition in danish"):
  with members:
    - "word translation without parallel data"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "polyglot distributed word representations for multilingual nlp"
    - "natural language processing almost from scratch"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "tnt a statistical part of speech tagger"

Connected through hyperedge HE-2 (induced by source paper "zero resource cross lingual named entity recognition"):
  with members:
    - "a finnish news corpus for named entity recognition"
    - "zero resource multilingual model transfer learning what to share"
    - "an iterative closest point method for unsupervised word translation"
    - "deep contextualized word representations"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "fast and accurate entity recognition with iterated dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "feature dependent confusion matrices for low resource ner labeling with noisy labels"):
  with members:
    - "handling noisy labels for robustly learning from self training data for low resource sequence labeling"
    - "training a neural network in a low resource setting on automatically annotated noisy data"
    - "learning word vectors for 157 languages"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "learning with noise enhance distantly supervised relation extraction with dynamic transition matrix"
    - "learning from noisy large scale datasets with minimal supervision"
    - "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
    - "training convolutional networks with noisy labels"

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)