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: "riga at semeval 2016 task 8 impact of smatch extensions and character level neural translation on amr parsing accuracy"
  Abstract: Two extensions to the AMR smatch scoring script are presented. The first extension com-bines the smatch scoring script with the C6.0 rule-based classifier to produce a human-readable report on the error patterns frequency observed in the scored AMR graphs. This first extension results in 4% gain over the state-of-art CAMR baseline parser by adding to it a manually crafted wrapper fixing the identified CAMR parser errors. The second extension combines a per-sentence smatch with an en-semble method for selecting the best AMR graph among the set of AMR graphs for the same sentence. This second modification au-tomatically yields further 0.4% gain when ap-plied to outputs of two nondeterministic AMR parsers: a CAMR+wrapper parser and a novel character-level neural translation AMR parser. For AMR parsing task the character-level neural translation attains surprising 7% gain over the carefully optimized word-level neural translation. Overall, we achieve smatch F1=62% on the SemEval-2016 official scor-ing set and F1=67% on the LDC2015E86 test set.

Connected through hyperedge HE-1 (induced by source paper "amr parsing as sequence to graph transduction"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "simpler but more accurate semantic dependency parsing"

Connected through hyperedge HE-2 (induced by source paper "broad coverage semantic parsing as transduction"):
  with members:
    - "core semantic first a top down approach for amr parsing"
    - "compositional semantic parsing across graphbanks"
    - "semeval 2019 task 1 cross lingual semantic parsing with ucca"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "hlt suda at semeval 2019 task 1 ucca graph parsing as constituent tree parsing"
    - "learning joint semantic parsers from disjoint data"
    - "multitask parsing across semantic representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "core semantic first a top down approach for amr parsing"):
  with members:
    - "compositional semantic parsing across graphbanks"
    - "rewarding smatch transition based amr parsing with reinforcement learning"
    - "toward abstractive summarization using semantic representations"
    - "amr dependency parsing with a typed semantic algebra"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "linguistically informed self attention for semantic role labeling"
    - "guided neural language generation for abstractive summarization using abstract meaning representation"

Connected through hyperedge HE-4 (induced by source paper "automatic accuracy prediction for amr parsing"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "neural amr sequence to sequence models for parsing and generation"
    - "the meaning factory at semeval 2017 task 9 producing amrs with neural semantic parsing"
    - "text summarization using abstract meaning representation"
    - "an incremental parser for abstract meaning representation"

Connected through hyperedge HE-5 (induced by source paper "amr normalization for fairer evaluation"):
  with members:
    - "neural text generation from rich semantic representations"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "neural amr sequence to sequence models for parsing and generation"
    - "an incremental parser for abstract meaning representation"

Connected through hyperedge HE-6 (induced by source paper "robust incremental neural semantic graph parsing"):
  with members:
    - "addressing the data sparsity issue in neural amr parsing"
    - "language to logical form with neural attention"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "an incremental parser for abstract meaning representation"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "incremental parsing with minimal features using bi directional lstm"
    - "skip thought vectors"

Connected through hyperedge HE-7 (induced by source paper "neural amr sequence to sequence models for parsing and generation"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "addressing the data sparsity issue in neural amr parsing"
    - "an incremental parser for abstract meaning representation"
    - "amr to text generation as a traveling salesman problem"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "neural semantic parsing by character based translation experiments with abstract meaning representations"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "the meaning factory at semeval 2017 task 9 producing amrs with neural semantic parsing"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "language to logical form with neural attention"
    - "an incremental parser for abstract meaning representation"

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)