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: "learning when to trust distant supervision an application to low resource pos tagging using cross lingual projection"
  Abstract: Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly. In this paper, we introduce a novel approach to sequence tagging that learns to correct the errors from cross-lingual projection using an explicit debiasing layer. This is framed as joint learning over two corpora, one tagged with gold standard and the other with projected tags. We evaluated with only 1,000 tokens tagged with gold standard tags, along with more plentiful parallel data. Our system equals or exceeds the state-of-the-art on eight simulated low-resource settings, as well as two real low-resource languages, Malagasy and Kinyarwanda.

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"
    - "massively multilingual transfer for ner"

Connected through hyperedge HE-2 (induced by source paper "exploring multilingual syntactic sentence representations"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "assessing bert s syntactic abilities"
    - "universal dependency parsing from scratch"
    - "filtering and mining parallel data in a joint multilingual space"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"

Connected through hyperedge HE-3 (induced by source paper "massively multilingual transfer for ner"):
  with members:
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "learning how to active learn a deep reinforcement learning approach"
    - "snorkel rapid training data creation with weak supervision"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-4 (induced by source paper "you write like you eat stylistic variation as a predictor of social stratification"):
  with members:
    - "bleaching text abstract features for cross lingual gender prediction"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "understanding and overcoming biases in customer reviews"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "distant supervision for emotion detection using facebook reactions"
    - "computational sociolinguistics a survey"
    - "adam a method for stochastic optimization"
    - "the readability of tweets and their geographic correlation with education"

Connected through hyperedge HE-5 (induced by source paper "feature dependent confusion matrices for low resource ner labeling with noisy labels"):
  with members:
    - "multilingual ner transfer for low resource languages"
    - "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"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-6 (induced by source paper "neural cross lingual named entity recognition with minimal resources"):
  with members:
    - "deep contextualized word representations"
    - "attention is all you need"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "semi supervised sequence tagging with bidirectional language models"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-7 (induced by source paper "strong baselines for neural semi supervised learning under domain shift"):
  with members:
    - "on the state of the art of evaluation in neural language models"
    - "stronger baselines for trustable results in neural machine translation"
    - "model transfer for tagging low resource languages using a bilingual dictionary"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "to normalize or not to normalize the impact of normalization on part of speech tagging"
    - "predicting the effectiveness of self training application to sentiment classification"
    - "online updating of word representations for part of speech tagging"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-8 (induced by source paper "distant supervision from disparate sources for low resource part of speech tagging"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "modeling source syntax for neural machine translation"
    - "enriching word vectors with subword information"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "linguistic input features improve neural machine translation"
    - "polyglot distributed word representations for multilingual nlp"

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)