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 dependency parsing with a typed semantic algebra"
  Abstract: We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.

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"
    - "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"
    - "cross lingual abstract meaning representation parsing"

Connected through hyperedge HE-2 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep rnns encode soft hierarchical syntax"

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"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "semantic neural machine translation using amr"):
  with members:
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "a graph to sequence model for amr to text generation"
    - "amr parsing as graph prediction with latent alignment"
    - "sentence state lstm for text representation"
    - "a challenge set approach to evaluating machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "semantic graph parsing with recurrent neural network dag grammars"):
  with members:
    - "exploring neural methods for parsing discourse representation structures"
    - "learning deep generative models of graphs"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "amr parsing as graph prediction with latent alignment"
    - "towards universal semantic tagging"
    - "robust incremental neural semantic graph parsing"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-6 (induced by source paper "compositional semantic parsing across graphbanks"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "learning joint semantic parsers from disjoint data"
    - "parser training with heterogeneous treebanks"
    - "multitask parsing across semantic representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr parsing as graph prediction with latent alignment"
    - "simpler but more accurate semantic dependency parsing"
    - "robust incremental neural semantic graph parsing"

Connected through hyperedge HE-7 (induced by source paper "automatic accuracy prediction for amr parsing"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "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"
    - "riga at semeval 2016 task 8 impact of smatch extensions and character level neural translation on amr parsing accuracy"

Connected through hyperedge HE-8 (induced by source paper "sembleu a robust metric for amr parsing evaluation"):
  with members:
    - "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"
    - "amr parsing using stack lstms"
    - "robust incremental neural semantic graph parsing"
    - "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)