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: "sentence ordering and coherence modeling using recurrent neural networks"
  Abstract: Modeling the structure of coherent texts is a key NLP problem. The task of coherently organizing a given set of sentences has been commonly used to build and evaluate models that understand such structure. We propose an end-to-end unsupervised deep learning approach based on the set-to-sequence framework to address this problem. Our model strongly outperforms prior methods in the order discrimination task and a novel task of ordering abstracts from scientific articles. Furthermore, our work shows that useful text representations can be obtained by learning to order sentences. Visualizing the learned sentence representations shows that the model captures high-level logical structure in paragraphs. Our representations perform comparably to state-of-the-art pre-training methods on sentence similarity and paraphrase detection tasks.

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 "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"
    - "sort story sorting jumbled images and captions into stories"
    - "neural sentence ordering"
    - "neural net models for open domain discourse coherence"

Connected through hyperedge HE-3 (induced by source paper "graph based neural sentence ordering"):
  with members:
    - "semantic neural machine translation using amr"
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a graph to sequence model for amr to text generation"
    - "sentence state lstm for text representation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for coherence modeling"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "coherence modeling of asynchronous conversations a neural entity grid approach"
    - "discourse coherence in the wild a dataset evaluation and methods"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "neural automated essay scoring and coherence modeling for adversarially crafted input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "auxiliary objectives for neural error detection models"
    - "neural net models for open domain discourse coherence"

Connected through hyperedge HE-5 (induced by source paper "a cross domain transferable neural coherence model"):
  with members:
    - "coherence modeling of asynchronous conversations a neural entity grid approach"
    - "learning to write with cooperative discriminators"
    - "deep contextualized word representations"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "axiomatic attribution for deep networks"
    - "neural net models for open domain discourse coherence"
    - "skip thought vectors"

Connected through hyperedge HE-6 (induced by source paper "picking apart story salads"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "modeling semantic plausibility by injecting world knowledge"
    - "implicit argument prediction with event knowledge"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "learning paraphrastic sentence embeddings from back translated bitext"
    - "event representations with tensor based compositions"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-7 (induced by source paper "quantum statistics inspired neural attention"):
  with members:
    - "analyzing uncertainty in neural machine translation"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "enhancing sentence embedding with generalized pooling"
    - "translating phrases in neural machine translation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "structured attention networks"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "end to end memory networks"

Connected through hyperedge HE-8 (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"
    - "neural sentence ordering"
    - "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"

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)