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: "improving neural parsing by disentangling model combination and reranking effects"
  Abstract: Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficult, they have primarily been used to rescore candidate outputs from base parsers in which decoding is more straightforward. We first present an algorithm for direct search in these generative models. We then demonstrate that the rescoring results are at least partly due to implicit model combination rather than reranking effects. Finally, we show that explicit model combination can improve performance even further, resulting in new state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data and 94.66 F1 when using external data.

Connected through hyperedge HE-1 (induced by source paper "scalable syntax aware language models using knowledge distillation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "assessing bert s syntactic abilities"
    - "universal transformers"
    - "finding syntax in human encephalography with beam search"
    - "distilling knowledge for search based structured prediction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "concurrent parsing of constituency and dependency"):
  with members:
    - "sequence labeling parsing by learning across representations"
    - "what s going on in neural constituency parsers an analysis"
    - "two local models for neural constituent parsing"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"

Connected through hyperedge HE-4 (induced by source paper "cross domain generalization of neural constituency parsers"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "extending a parser to distant domains using a few dozen partially annotated examples"
    - "multilingual constituency parsing with self attention and pre training"
    - "deep contextualized word representations"
    - "straight to the tree constituency parsing with neural syntactic distance"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "constituency parsing with a self attentive encoder"
    - "in order transition based constituent parsing"

Connected through hyperedge HE-5 (induced by source paper "neural generative rhetorical structure parsing"):
  with members:
    - "a unified linear time framework for sentence level discourse parsing"
    - "variational smoothing in recurrent neural network language models"
    - "neural discourse structure for text categorization"
    - "cross lingual rst discourse parsing"
    - "effective inference for generative neural parsing"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "a latent variable recurrent neural network for discourse relation language models"

Connected through hyperedge HE-6 (induced by source paper "global reasoning over database structures for text to sql parsing"):
  with members:
    - "grammar based neural text to sql generation"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "question answering by reasoning across documents with graph convolutional networks"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-7 (induced by source paper "direct output connection for a high rank language model"):
  with members:
    - "deep contextualized word representations"
    - "constituency parsing with a self attentive encoder"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "selective encoding for abstractive sentence summarization"
    - "input to output gate to improve rnn language models"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"

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"
    - "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)