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 sentence ordering"
  Abstract: Sentence ordering is a general and critical task for natural language generation applications. Previous works have focused on improving its performance in an external, downstream task, such as multi-document summarization. Given its importance, we propose to study it as an isolated task. We collect a large corpus of academic texts, and derive a data driven approach to learn pairwise ordering of sentences, and validate the efficacy with extensive experiments. Source codes and dataset of this paper will be made publicly available.

Connected through hyperedge HE-1 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "a unified linear time framework for sentence level discourse parsing"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "evaluating discourse in structured text representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-2 (induced by source paper "evaluation benchmarks and learning criteriafor discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "discourse aware neural rewards for coherent text generation"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-3 (induced by source paper "structbert incorporating language structures into pre training for deep language understanding"):
  with members:
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "discriminative sentence modeling for story ending prediction"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self normalizing neural networks"
    - "attention over attention neural networks for reading comprehension"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "generating and exploiting large scale pseudo training data for zero pronoun resolution"
    - "bidirectional attention flow for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-5 (induced by source paper "deep attentive ranking networks for learning to order sentences"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "context is key new approaches to neural coherence modeling"
    - "an efficient framework for learning sentence representations"
    - "attention is all you need"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "sort story sorting jumbled images and captions into stories"
    - "neural net models for open domain discourse coherence"

Connected through hyperedge HE-6 (induced by source paper "learning sentence embeddings for coherence modelling and beyond"):
  with members:
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "get to the point summarization with pointer generator networks"
    - "text coherence analysis based on deep neural network"
    - "a deep reinforced model for abstractive summarization"
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "bag of tricks for efficient text classification"
    - "end to end neural sentence ordering using pointer network"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"

Connected through hyperedge HE-7 (induced by source paper "context is key new approaches to neural coherence modeling"):
  with members:
    - "sentence ordering and coherence modeling using recurrent neural networks"
    - "neural net models for open domain discourse coherence"
    - "catching the drift probabilistic content models with applications to generation and summarization"
    - "inferring strategies for sentence ordering in multidocument news summarization"

Connected through hyperedge HE-8 (induced by source paper "sentence ordering and coherence modeling using recurrent neural networks"):
  with members:
    - "classify or select neural architectures for extractive document summarization"
    - "neural summarization by extracting sentences and words"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "neural net models for open domain discourse coherence"
    - "unsupervised visual representation learning by context prediction"
    - "visualizing and understanding neural models in nlp"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "skip thought vectors"

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)