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: "grammar as a foreign language"
  Abstract: Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades. As a result, the most accurate parsers are domain specific, complex, and inefficient. In this paper we show that the domain agnostic attention-enhanced sequence-to-sequence model achieves state-of-the-art results on the most widely used syntactic constituency parsing dataset, when trained on a large synthetic corpus that was annotated using existing parsers. It also matches the performance of standard parsers when trained only on a small human-annotated dataset, which shows that this model is highly data-efficient, in contrast to sequence-to-sequence models without the attention mechanism. Our parser is also fast, processing over a hundred sentences per second with an unoptimized CPU implementation.

Connected through hyperedge HE-1 (induced by source paper "parsing as pretraining"):
  with members:
    - "assessing bert s syntactic abilities"
    - "viable dependency parsing as sequence labeling"
    - "better faster stronger sequence tagging constituent parsers"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "designing and interpreting probes with control tasks"
    - "the non utility of structural features in bilstm based dependency parsers"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "linguistic knowledge and transferability of contextual representations"

Connected through hyperedge HE-2 (induced by source paper "learning to multi task learn for better neural machine translation"):
  with members:
    - "autosem automatic task selection and mixing in multi task learning"
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "learning to reweight examples for robust deep learning"
    - "scheduled multi task learning from syntax to translation"
    - "learning to multitask"
    - "opennmt open source toolkit for neural machine translation"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-3 (induced by source paper "auto completion of user interface layout design using transformer based tree decoders"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "tree to tree neural networks for program translation"
    - "syntax directed variational autoencoder for structured data"
    - "automatic graphics program generation using attention based hierarchical decoder"
    - "attention is all you need"
    - "pix2code generating code from a graphical user interface screenshot"
    - "a neural representation of sketch drawings"
    - "language to logical form with neural attention"

Connected through hyperedge HE-4 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "learning to infer and execute 3d shape programs"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "grains generative recursive autoencoders for indoor scenes"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-5 (induced by source paper "unpaired image to speech synthesis with multimodal information bottleneck"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "fitting new speakers based on a short untranscribed sample"
    - "recurrent fusion network for image captioning"
    - "voiceloop voice fitting and synthesis via a phonological loop"
    - "letter based speech recognition with gated convnets"
    - "attention is all you need"
    - "improving end to end speech recognition with policy learning"

Connected through hyperedge HE-6 (induced by source paper "semantic parsing with dual learning"):
  with members:
    - "agentgraph towards universal dialogue management with structured deep reinforcement learning"
    - "a hierarchical decoding model for spoken language understanding from unaligned data"
    - "question generation from sql queries improves neural semantic parsing"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "dual supervised learning"

Connected through hyperedge HE-7 (induced by source paper "generating logical forms from graph representations of text and entities"):
  with members:
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "self attention with relative position representations"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (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"

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)