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: "effective inference for generative neural parsing"
  Abstract: Generative neural models have recently achieved state-of-the-art results for constituency parsing. However, without a feasible search procedure, their use has so far been limited to reranking the output of external parsers in which decoding is more tractable. We describe an alternative to the conventional action-level beam search used for discriminative neural models that enables us to decode directly in these generative models. We then show that by improving our basic candidate selection strategy and using a coarse pruning function, we can improve accuracy while exploring significantly less of the search space. Applied to the model of Choe and Charniak (2016), our inference procedure obtains 92.56 F1 on section 23 of the Penn Treebank, surpassing prior state-of-the-art results for single-model systems.

Connected through hyperedge HE-1 (induced by source paper "automatic business process structure discovery using ordered neurons lstm a preliminary study"):
  with members:
    - "linguistic versus latent relations for modeling coherent flow in paragraphs"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "attention is all you need"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (induced by source paper "parsing all syntax and semantics dependencies and spans"):
  with members:
    - "syntax aware neural semantic role labeling with supertags"
    - "dependency or span end to end uniform semantic role labeling"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what s going on in neural constituency parsers an analysis"
    - "a span selection model for semantic role labeling"
    - "multilingual constituency parsing with self attention and pre training"
    - "jointly predicting predicates and arguments in neural semantic role labeling"

Connected through hyperedge HE-6 (induced by source paper "limit bert linguistic informed multi task bert"):
  with members:
    - "semantics aware bert for language understanding"
    - "dependency or span end to end uniform semantic role labeling"
    - "parsing all syntax and semantics dependencies and spans"
    - "multi task deep neural networks for natural language understanding"
    - "head driven phrase structure grammar parsing on penn treebank"
    - "i know what you want semantic learning for text comprehension"
    - "what s going on in neural constituency parsers an analysis"
    - "multilingual constituency parsing with self attention and pre training"

Connected through hyperedge HE-7 (induced by source paper "neural language models as psycholinguistic subjects representations of syntactic state"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "a neural model of adaptation in reading"
    - "what do rnn language models learn about filler gap dependencies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "exploring the syntactic abilities of rnns with multi task learning"

Connected through hyperedge HE-8 (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"
    - "improving neural parsing by disentangling model combination and reranking effects"
    - "neural discourse structure for text categorization"
    - "cross lingual rst discourse parsing"
    - "recurrent neural network grammars"
    - "globally normalized transition based neural networks"
    - "a latent variable recurrent neural network for discourse relation language models"

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)