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: "addressing the data sparsity issue in neural amr parsing"
  Abstract: Neural attention models have achieved great success in different NLP tasks. How- ever, they have not fulfilled their promise on the AMR parsing task due to the data sparsity issue. In this paper, we de- scribe a sequence-to-sequence model for AMR parsing and present different ways to tackle the data sparsity problem. We show that our methods achieve significant improvement over a baseline neural atten- tion model and our results are also compet- itive against state-of-the-art systems that do not use extra linguistic resources.

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 "end to end content and plan selection for data to text generation"):
  with members:
    - "natural language generation by hierarchical decoding with linguistic patterns"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "unsupervised natural language generation with denoising autoencoders"
    - "findings of the e2e nlg challenge"
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "get to the point summarization with pointer generator networks"
    - "the e2e dataset new challenges for end to end generation"

Connected through hyperedge HE-5 (induced by source paper "polyglot semantic parsing in apis"):
  with members:
    - "a language for function signature representations"
    - "the code2text challenge text generation in source libraries"
    - "a parallel corpus of python functions and documentation strings for automated code documentation and code generation"
    - "neural machine translation for low resource languages"
    - "a survey of machine learning for big code and naturalness"
    - "neural semantic parsing over multiple knowledge bases"
    - "function assistant a tool for nl querying of apis"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-6 (induced by source paper "amr parsing as graph prediction with latent alignment"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "text summarization using abstract meaning representation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "language to logical form with neural attention"
    - "learning executable semantic parsers for natural language understanding"
    - "an incremental parser for abstract meaning representation"

Connected through hyperedge HE-7 (induced by source paper "cross lingual semantic parsing"):
  with members:
    - "end to end neural coreference resolution"
    - "language to logical form with neural attention"
    - "multilingual open relation extraction using cross lingual projection"
    - "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-8 (induced by source paper "an amr aligner tuned by transition based parser"):
  with members:
    - "amr parsing as graph prediction with latent alignment"
    - "neural amr sequence to sequence models for parsing and generation"
    - "amr parsing using stack lstms"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "an incremental parser for abstract meaning representation"
    - "transition based dependency parsing with stack long short term memory"

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)