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: "massively multilingual transfer for ner"
  Abstract: In cross-lingual transfer, NLP models over one or more source languages are applied to a low-resource target language. While most prior work has used a single source model or a few carefully selected models, here we consider a `massive' setting with many such models. This setting raises the problem of poor transfer, particularly from distant languages. We propose two techniques for modulating the transfer, suitable for zero-shot or few-shot learning, respectively. Evaluating on named entity recognition, 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 "distant supervision and noisy label learning for low resource named entity recognition a study on hausa and yor ub a"):
  with members:
    - "named entity recognition with partially annotated training data"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "low resource name tagging learned with weakly labeled data"
    - "cloze driven pretraining of self attention networks"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "feature dependent confusion matrices for low resource ner labeling with noisy labels"
    - "handling noisy labels for robustly learning from self training data for low resource sequence labeling"

Connected through hyperedge HE-2 (induced by source paper "improving candidate generation for low resource cross lingual entity linking"):
  with members:
    - "towards zero resource cross lingual entity linking"
    - "choosing transfer languages for cross lingual learning"
    - "joint multilingual supervision for cross lingual entity linking"
    - "unsupervised multilingual word embeddings"
    - "bootstrapping transliteration with constrained discovery for low resource languages"
    - "zero shot neural transfer for cross lingual entity linking"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "one for all towards language independent named entity linking"

Connected through hyperedge HE-3 (induced by source paper "learning to contextually aggregate multi source supervision for sequence labeling"):
  with members:
    - "crossweigh training named entity tagger from imperfect annotations"
    - "multinomial adversarial networks for multi domain text classification"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "semi supervised sequence modeling with cross view training"
    - "neural adaptation layers for cross domain named entity recognition"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "who said what modeling individual labelers improves classification"

Connected through hyperedge HE-4 (induced by source paper "characterizing collective attention via descriptor context a case study of public discussions of crisis events"):
  with members:
    - "pomo generating entity specific post modifiers in context"
    - "the effect of collective attention on controversial debates on social media"
    - "credibility and dynamics of collective attention"
    - "globally normalized transition based neural networks"
    - "rising tides or rising stars dynamics of shared attention on twitter during media events"
    - "processing social media messages in mass emergency a survey"
    - "dynamical classes of collective attention in twitter"
    - "novelty and collective attention"

Connected through hyperedge HE-5 (induced by source paper "what matters for neural cross lingual named entity recognition an empirical analysis"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "word translation without parallel data"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "enriching word vectors with subword information"
    - "neural architectures for named entity recognition"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

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)