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 open relation extraction using cross lingual projection"
  Abstract: Open domain relation extraction systems identify relation and argument phrases in a sentence without relying on any underlying schema. However, current state-of-the-art relation extraction systems are available only for English because of their heavy reliance on linguistic tools such as part-of-speech taggers and dependency parsers. We present a cross-lingual annotation projection method for language independent relation extraction. We evaluate our method on a manually annotated test set and present results on three typologically different languages. We release these manual annotations and extracted relations in 61 languages from Wikipedia.

Connected through hyperedge HE-1 (induced by source paper "zero shot cross lingual transfer with meta learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "x wikire a large multilingual resource for relation extraction as machine comprehension"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "on first order meta learning algorithms"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"

Connected through hyperedge HE-2 (induced by source paper "neural cross lingual relation extraction based on bilingual word embedding mapping"):
  with members:
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "multilingual relation extraction using compositional universal schema"
    - "combining neural networks and log linear models to improve relation extraction"

Connected through hyperedge HE-3 (induced by source paper "x wikire a large multilingual resource for relation extraction as machine comprehension"):
  with members:
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "zero shot relation extraction via reading comprehension"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "word translation without parallel data"
    - "newsqa a machine comprehension dataset"
    - "wikireading a novel large scale language understanding task over wikipedia"

Connected through hyperedge HE-4 (induced by source paper "cross lingual semantic parsing"):
  with members:
    - "end to end neural coreference resolution"
    - "addressing the data sparsity issue in neural amr parsing"
    - "language to logical form with neural attention"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"
    - "cross lingual pseudo projected expectation regularization for weakly supervised learning"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

Connected through hyperedge HE-5 (induced by source paper "a framework for enriching lexical semantic resources with distributional semantics"):
  with members:
    - "unsupervised knowledge free and interpretable word sense disambiguation"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "do multi sense embeddings improve natural language understanding"
    - "retrofitting word vectors to semantic lexicons"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "multiwiki interlingual text passage alignment in wikipedia"):
  with members:
    - "growing wikipedia across languages via recommendation"
    - "analysing temporal evolution of interlingual wikipedia article pairs"
    - "multilinguals and wikipedia editing"
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"

Connected through hyperedge HE-7 (induced by source paper "multilingual relation extraction using compositional universal schema"):
  with members:
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "traversing knowledge graphs in vector space"
    - "when are tree structures necessary for deep learning of representations"
    - "compositional vector space models for knowledge base completion"
    - "adam a method for stochastic optimization"
    - "retrofitting word vectors to semantic lexicons"
    - "multilingual models for compositional distributed semantics"

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)