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: "cross lingual annotation projection of semantic roles"
  Abstract: This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection, phrased as a graph optimization problem. It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources. Within this framework, we present projection models that exploit lexical and syntactic information. We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision German semantic role annotation both for manually and automatically annotated English data.

Connected through hyperedge HE-1 (induced by source paper "watset local global graph clustering with applications in sense and frame induction"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "unsupervised semantic frame induction using triclustering"
    - "a framework for enriching lexical semantic resources with distributional semantics"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "improving hypernymy extraction with distributional semantic classes"
    - "watset automatic induction of synsets from a graph of synonyms"
    - "human and machine judgements for russian semantic relatedness"

Connected through hyperedge HE-2 (induced by source paper "a discriminative neural model for cross lingual word alignment"):
  with members:
    - "adding interpretable attention to neural translation models improves word alignment"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "what does attention in neural machine translation pay attention to"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "entity projection via machine translation for cross lingual ner"):
  with members:
    - "pioner datasets and baselines for armenian named entity recognition"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "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"
    - "the iit bombay english hindi parallel corpus"
    - "word translation without parallel data"
    - "neural architectures for named entity recognition"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "cross lingual transfer of semantic roles from raw text to semantic roles"):
  with members:
    - "polyglot semantic role labeling"
    - "unimorph 2 0 universal morphology"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "transferring semantic roles using translation and syntactic information"
    - "neural semantic role labeling with dependency path embeddings"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"
    - "a universal part of speech tagset"

Connected through hyperedge HE-5 (induced by source paper "modeling language variation and universals a survey on typological linguistics for natural language processing"):
  with members:
    - "morphological word embeddings"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "a deep generative model of vowel formant typology"
    - "deep contextualized word representations"
    - "deep probabilistic logic a unifying framework for indirect supervision"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "past present future a computational investigation of the typology of tense in 1000 languages"

Connected through hyperedge HE-6 (induced by source paper "neural task representations as weak supervision for model agnostic cross lingual transfer"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "reinforced mnemonic reader for machine comprehension"
    - "cross lingual models of word embeddings an empirical comparison"
    - "cross stitch networks for multi task learning"
    - "trace norm regularised deep multi task learning"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "how transferable are neural networks in nlp applications"
    - "achieving human parity in conversational speech recognition"

Connected through hyperedge HE-7 (induced by source paper "past present future a computational investigation of the typology of tense in 1000 languages"):
  with members:
    - "learning language representations for typology prediction"
    - "comparing fifty natural languages and twelve genetic languages using word embedding language divergence weld as a quantitative measure of language distance"
    - "faster decoding for subword level phrase based smt between related languages"
    - "a character level decoder without explicit segmentation for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "inducing multilingual text analysis tools using bidirectional recurrent neural networks"):
  with members:
    - "on the properties of neural machine translation encoder decoder approaches"
    - "sequence to sequence learning with neural networks"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"
    - "a universal part of speech tagset"
    - "tnt a statistical part of speech tagger"

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)