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: "distant supervision from disparate sources for low resource part of speech tagging"
  Abstract: We introduce DsDs: a cross-lingual neural part-of-speech tagger that learns from disparate sources of distant supervision, and realistically scales to hundreds of low-resource languages. The model exploits annotation projection, instance selection, tag dictionaries, morphological lexicons, and distributed representations, all in a uniform framework. The approach is simple, yet surprisingly effective, resulting in a new state of the art without access to any gold annotated data.

Connected through hyperedge HE-1 (induced by source paper "deep contextualized self training for low resource dependency parsing"):
  with members:
    - "viable dependency parsing as sequence labeling"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "no training required exploring random encoders for sentence classification"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "choosing transfer languages for cross lingual learning"):
  with members:
    - "xnmt the extensible neural machine translation toolkit"
    - "neural factor graph models for cross lingual morphological tagging"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "zero shot neural transfer for cross lingual entity linking"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "rapid adaptation of neural machine translation to new languages"

Connected through hyperedge HE-3 (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"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning crosslingual word embeddings without bilingual corpora"

Connected through hyperedge HE-5 (induced by source paper "cross lingual parsing with polyglot training and multi treebank learning a faroese case study"):
  with members:
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "parser training with heterogeneous treebanks"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "82 treebanks 34 models universal dependency parsing with multi treebank models"
    - "cross lingual dependency parsing with late decoding for truly low resource languages"

Connected through hyperedge HE-6 (induced by source paper "instance based transfer learning for multilingual deep retrieval"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "gromov wasserstein alignment of word embedding spaces"
    - "stochastic negative mining for learning with large output spaces"
    - "unsupervised multilingual word embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "fine tuned language models for text classification"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "cross lingual transfer learning for multilingual task oriented dialog"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "polyglot contextual representations improve crosslingual transfer"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "pytext a seamless path from nlp research to production"
    - "on the strength of character language models for multilingual named entity recognition"
    - "parameter sharing between dependency parsers for related languages"
    - "zero shot cross lingual classification using multilingual neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "the best of both worlds lexical resources to improve low resource part of speech tagging"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "evaluating the utility of hand crafted features in sequence labelling"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "improved neural machine translation with a syntax aware encoder and decoder"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "representation of linguistic form and function in recurrent neural networks"

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)