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: "robust subgraph generation improves abstract meaning representation parsing"
  Abstract: The Abstract Meaning Representation (AMR) is a representation for open-domain rich semantics, with potential use in fields like event extraction and machine translation. Node generation, typically done using a simple dictionary lookup, is currently an important limiting factor in AMR parsing. We propose a small set of actions that derive AMR subgraphs by transformations on spans of text, which allows for more robust learning of this stage. Our set of construction actions generalize better than the previous approach, and can be learned with a simple classifier. We improve on the previous state-of-the-art result for AMR parsing, boosting end-to-end performance by 3 F$_1$ on both the LDC2013E117 and LDC2014T12 datasets.

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 "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-3 (induced by source paper "scene graph parsing as dependency parsing"):
  with members:
    - "a transition based directed acyclic graph parser for ucca"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "neural amr sequence to sequence models for parsing and generation"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "robust incremental neural semantic graph parsing"
    - "recurrent multimodal interaction for referring image segmentation"
    - "one model to learn them all"

Connected through hyperedge HE-4 (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"
    - "addressing the data sparsity issue in neural amr parsing"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a transition based directed acyclic graph parser for ucca"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "hume human ucca based evaluation of machine translation"
    - "greedy joint syntactic semantic parsing with stack lstms"
    - "globally normalized transition based neural networks"
    - "an incremental parser for abstract meaning representation"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "parsing as reduction"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "amr parsing using stack lstms"):
  with members:
    - "recurrent neural network grammars"
    - "an incremental parser for abstract meaning representation"
    - "neural architectures for named entity recognition"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "transition based dependency parsing with stack long short term memory"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "spice semantic propositional image caption evaluation"):
  with members:
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "from images to sentences through scene description graphs using commonsense reasoning and knowledge"
    - "language models for image captioning the quirks and what works"
    - "show attend and tell neural image caption generation with visual attention"
    - "exploring nearest neighbor approaches for image captioning"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"

Connected through hyperedge HE-8 (induced by source paper "an incremental parser for abstract meaning representation"):
  with members:
    - "riga at semeval 2016 task 8 impact of smatch extensions and character level neural translation on amr parsing accuracy"
    - "using syntax based machine translation to parse english into abstract meaning representation"
    - "parser for abstract meaning representation using learning to search"
    - "search based structured prediction"

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)