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: "yara parser a fast and accurate dependency parser"
  Abstract: Dependency parsers are among the most crucial tools in natural language processing as they have many important applications in downstream tasks such as information retrieval, machine translation and knowledge acquisition. We introduce the Yara Parser, a fast and accurate open-source dependency parser based on the arc-eager algorithm and beam search. It achieves an unlabeled accuracy of 93.32 on the standard WSJ test set which ranks it among the top dependency parsers. At its fastest, Yara can parse about 4000 sentences per second when in greedy mode (1 beam). When optimizing for accuracy (using 64 beams and Brown cluster features), Yara can parse 45 sentences per second. The parser can be trained on any syntactic dependency treebank and dierent options are provided in order to make it more flexible and tunable for specific tasks. It is released with the Apache version 2.0 license and can be used for both commercial and academic purposes. The parser can be found at https: //github.com/yahoo/YaraParser.

Connected through hyperedge HE-1 (induced by source paper "a generative model for punctuation in dependency trees"):
  with members:
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "recurrent neural network grammars"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "unsupervised neural hidden markov models"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-2 (induced by source paper "dissecting content and context in argumentative relation analysis"):
  with members:
    - "parsing argumentation structures in persuasive essays"
    - "distributed representations of words and phrases and their compositionality"
    - "a pdtb styled end to end discourse parser"

Connected through hyperedge HE-3 (induced by source paper "transition based parsing with lighter feed forward networks"):
  with members:
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "a non projective greedy dependency parser with bidirectional lstms"
    - "arc swift a novel transition system for dependency parsing"
    - "globally normalized transition based neural networks"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-4 (induced by source paper "robust cross lingual hypernymy detection using dependency context"):
  with members:
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "representing verbs with rich contexts an evaluation on verb similarity"
    - "many languages one parser"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-5 (induced by source paper "a non projective greedy dependency parser with bidirectional lstms"):
  with members:
    - "universal unsupervised rule based uncovered sentiment analysis"
    - "a full non monotonic transition system for unrestricted non projective parsing"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "one model two languages training bilingual parsers with harmonized treebanks"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "the galactic dependencies treebanks getting more data by synthesizing new languages"):
  with members:
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-7 (induced by source paper "transferring semantic roles using translation and syntactic information"):
  with members:
    - "a universal part of speech tagset"

Connected through hyperedge HE-8 (induced by source paper "cross lingual syntactic transfer with limited resources"):
  with members:
    - "cross lingual dependency parsing with late decoding for truly low resource languages"
    - "the galactic dependencies treebanks getting more data by synthesizing new languages"
    - "parsing universal dependencies without training"
    - "many languages one parser"

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)