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: "neural probabilistic model for non projective mst parsing"
  Abstract: In this paper, we propose a probabilistic parsing model, which defines a proper conditional probability distribution over non-projective dependency trees for a given sentence, using neural representations as inputs. The neural network architecture is based on bi-directional LSTM-CNNs which benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM and CNN. On top of the neural network, we introduce a probabilistic structured layer, defining a conditional log-linear model over non-projective trees. We evaluate our model on 17 different datasets, across 14 different languages. By exploiting Kirchhoff's Matrix-Tree Theorem (Tutte, 1984), the partition functions and marginals can be computed efficiently, leading to a straight-forward end-to-end model training procedure via back-propagation. Our parser achieves state-of-the-art parsing performance on nine datasets.

Connected through hyperedge HE-1 (induced by source paper "head driven phrase structure grammar parsing on penn treebank"):
  with members:
    - "lattice based transformer encoder for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "exploring recombination for efficient decoding of neural machine translation"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-2 (induced by source paper "a systematic study of leveraging subword information for learning word representations"):
  with members:
    - "morphological word embeddings"
    - "learning semantic representations for novel words leveraging both form and context"
    - "generalizing word embeddings using bag of subwords"
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "from characters to words to in between do we capture morphology"
    - "fast er exact decoding and global training for transition based dependency parsing via a minimal feature set"

Connected through hyperedge HE-3 (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-4 (induced by source paper "semi supervised sequence modeling with cross view training"):
  with members:
    - "strong baselines for neural semi supervised learning under domain shift"
    - "deep contextualized word representations"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "in order transition based constituent parsing"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial dropout for supervised and semi supervised learning"
    - "learned in translation contextualized word vectors"
    - "semi supervised multitask learning for sequence labeling"

Connected through hyperedge HE-5 (induced by source paper "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"):
  with members:
    - "inducing grammars with and for neural machine translation"
    - "deep contextualized word representations"
    - "structured attention networks"
    - "learning structured text representations"
    - "multi space variational encoder decoders for semi supervised labeled sequence transduction"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "crf autoencoder for unsupervised dependency parsing"

Connected through hyperedge HE-6 (induced by source paper "stack pointer networks for dependency parsing"):
  with members:
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "unsupervised ranking model for entity coreference resolution"
    - "globally normalized transition based neural networks"
    - "training with exploration improves a greedy stack lstm parser"
    - "dropout with expectation linear regularization"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distilling an ensemble of greedy dependency parsers into one mst parser"

Connected through hyperedge HE-7 (induced by source paper "sentence encoding with tree constrained relation networks"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"

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"
    - "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"
    - "incremental parsing with minimal features using bi directional lstm"

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)