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: "incremental parsing with minimal features using bi directional lstm"
  Abstract: Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We also introduce a novel transition system for constituency parsing which does not require binarization, and together with the above architecture, achieves state-of-the-art results among greedy parsers for both English and Chinese.

Connected through hyperedge HE-1 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method"):
  with members:
    - "bag of words as target for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "nonparametric neural networks"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "faster shift reduce constituent parsing with a non binary bottom up strategy"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "syntactic scaffolds for semantic structures"
    - "a tree based decoder for neural machine translation"
    - "constituency parsing with a self attentive encoder"
    - "efficient dependency guided named entity recognition"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "towards string to tree neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "water supply prediction based on initialized attention residual network"):
  with members:
    - "link prediction with mutual attention for text attributed networks"
    - "learning data augmentation strategies for object detection"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "random erasing data augmentation"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "unlexicalized transition based discontinuous constituency parsing"):
  with members:
    - "what s going on in neural constituency parsers an analysis"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"
    - "a minimal span based neural constituency parser"
    - "optimizing spectral learning for parsing"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-5 (induced by source paper "the non utility of structural features in bilstm based dependency parsers"):
  with members:
    - "recursive subtree composition in lstm based dependency parsing"
    - "what s going on in neural constituency parsers an analysis"
    - "transition based parsing with lighter feed forward networks"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "global transition based non projective dependency parsing"
    - "deep rnns encode soft hierarchical syntax"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "an investigation of the interactions between pre trained word embeddings character models and pos tags in dependency parsing"

Connected through hyperedge HE-6 (induced by source paper "discontinuous constituency parsing with a stack free transition system and a dynamic oracle"):
  with members:
    - "unlexicalized transition based discontinuous constituency parsing"
    - "dynamic oracles for top down and in order shift reduce constituent parsing"
    - "a minimal span based neural constituency parser"
    - "arc swift a novel transition system for dependency parsing"
    - "non projective dependency parsing with non local transitions"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"

Connected through hyperedge HE-7 (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-8 (induced by source paper "two local models for neural constituent parsing"):
  with members:
    - "in order transition based constituent parsing"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"
    - "a minimal span based neural constituency parser"
    - "neural probabilistic model for non projective mst parsing"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"

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)