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 minimal span based neural constituency parser"
  Abstract: In this work, we present a minimal neural model for constituency parsing based on independent scoring of labels and spans. We show that this model is not only compatible with classical dynamic programming techniques, but also admits a novel greedy top-down inference algorithm based on recursive partitioning of the input. We demonstrate empirically that both prediction schemes are competitive with recent work, and when combined with basic extensions to the scoring model are capable of achieving state-of-the-art single-model performance on the Penn Treebank (91.79 F1) and strong performance on the French Treebank (82.23 F1).

Connected through hyperedge HE-1 (induced by source paper "ecsp a new task for emotion cause span pair extraction and classification"):
  with members:
    - "emotion cause pair extraction a new task to emotion analysis in texts"
    - "rthn a rnn transformer hierarchical network for emotion cause extraction"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "a question answering approach to emotion cause extraction"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "on tree based neural sentence modeling"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"

Connected through hyperedge HE-3 (induced by source paper "generalizing natural language analysis through span relation representations"):
  with members:
    - "enriching pre trained language model with entity information for relation classification"
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simple bert models for relation extraction and semantic role labeling"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "unsupervised recurrent neural network grammars"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "text generation with exemplar based adaptive decoding"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"

Connected through hyperedge HE-5 (induced by source paper "palm a hybrid parser and language model"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "transformer xl attentive language models beyond a fixed length context"
    - "direct output connection for a high rank language model"
    - "backpropagating through structured argmax using a spigot"
    - "rational recurrences"
    - "syntactic scaffolds for semantic structures"
    - "learning joint semantic parsers from disjoint data"

Connected through hyperedge HE-6 (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-7 (induced by source paper "unsupervised question answering by cloze translation"):
  with members:
    - "evaluating rewards for question generation models"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "multiple attribute text style transfer"
    - "efficient and robust question answering from minimal context over documents"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-8 (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"

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)