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: "amr parsing using stack lstms"
  Abstract: We present a transition-based AMR parser that directly generates AMR parses from plain text. We use Stack-LSTMs to represent our parser state and make decisions greedily. In our experiments, we show that our parser achieves very competitive scores on English using only AMR training data. Adding additional information, such as POS tags and dependency trees, improves the results further.

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 "rewarding smatch transition based amr parsing with reinforcement learning"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"
    - "neural cross lingual entity linking"

Connected through hyperedge HE-5 (induced by source paper "sembleu a robust metric for amr parsing evaluation"):
  with members:
    - "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"
    - "robust incremental neural semantic graph parsing"
    - "an incremental parser for abstract meaning representation"

Connected through hyperedge HE-6 (induced by source paper "abstract meaning representation for multi document summarization"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "structure infused copy mechanisms for abstractive summarization"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "selective encoding for abstractive sentence summarization"
    - "get to the point summarization with pointer generator networks"
    - "amr to text generation with synchronous node replacement grammar"
    - "robust incremental neural semantic graph parsing"
    - "faithful to the original fact aware neural abstractive summarization"

Connected through hyperedge HE-7 (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"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "addressing the data sparsity issue in neural amr parsing"
    - "an incremental parser for abstract meaning representation"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-8 (induced by source paper "convamr abstract meaning representation parsing for legal document"):
  with members:
    - "riga at semeval 2016 task 8 impact of smatch extensions and character level neural translation on amr parsing accuracy"

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)