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: "a re ranking model for dependency parser with recursive convolutional neural network"
  Abstract: In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-the-art dependency parsing on both English and Chinese datasets.

Connected through hyperedge HE-1 (induced by source paper "global greedy dependency parsing"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "stack pointer networks for dependency parsing"

Connected through hyperedge HE-2 (induced by source paper "effective representation for easy first dependency parsing"):
  with members:
    - "exploring recombination for efficient decoding of neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "syntax directed attention for neural machine translation"
    - "fast and accurate neural word segmentation for chinese"
    - "easy first dependency parsing with hierarchical tree lstms"
    - "deep biaffine attention for neural dependency parsing"

Connected through hyperedge HE-3 (induced by source paper "neural reranking for named entity recognition"):
  with members:
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "deep learning applied to nlp"):
  with members:
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "comparing convolutional neural networks to traditional models for slot filling"
    - "dependency based convolutional neural networks for sentence embedding"
    - "modelling visualising and summarising documents with a single convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "easy first dependency parsing with hierarchical tree lstms"):
  with members:
    - "recurrent neural network grammars"
    - "skip thought vectors"
    - "transition based dependency parsing with stack long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree recurrent neural networks with application to language modeling"
    - "top down tree long short term memory networks"
    - "neural crf parsing"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-6 (induced by source paper "simple and accurate dependency parsing using bidirectional lstm feature representations"):
  with members:
    - "easy first dependency parsing with hierarchical tree lstms"
    - "training with exploration improves a greedy stack lstm parser"
    - "incremental parsing with minimal features using bi directional lstm"
    - "neural architectures for named entity recognition"
    - "transition based dependency parsing with stack long short term memory"
    - "visualizing and understanding recurrent networks"
    - "named entity recognition with bidirectional lstm cnns"
    - "natural language understanding with distributed representation"

Connected through hyperedge HE-7 (induced by source paper "bidirectional tree structured lstm with head lexicalization"):
  with members:
    - "recurrent neural network grammars"
    - "long short term memory over tree structures"
    - "compositional distributional semantics with long short term memory"
    - "transition based dependency parsing with stack long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree recurrent neural networks with application to language modeling"
    - "when are tree structures necessary for deep learning of representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "distilling an ensemble of greedy dependency parsers into one mst parser"):
  with members:
    - "sequence level knowledge distillation"
    - "globally normalized transition based neural networks"
    - "training with exploration improves a greedy stack lstm parser"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "transition based dependency parsing with stack long short term memory"
    - "structured training for neural network transition based parsing"
    - "grammar as a foreign language"

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)