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: "text generation from knowledge graphs with graph transformers"
  Abstract: Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce. In this work, we address the problem of generating coherent multi-sentence texts from the output of an information extraction system, and in particular a knowledge graph. Graphical knowledge representations are ubiquitous in computing, but pose a significant challenge for text generation techniques due to their non-hierarchical nature, collapsing of long-distance dependencies, and structural variety. We introduce a novel graph transforming encoder which can leverage the relational structure of such knowledge graphs without imposing linearization or hierarchical constraints. Incorporated into an encoder-decoder setup, we provide an end-to-end trainable system for graph-to-text generation that we apply to the domain of scientific text. Automatic and human evaluations show that our technique produces more informative texts which exhibit better document structure than competitive encoder-decoder methods.

Connected through hyperedge HE-1 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs"):
  with members:
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "graph transformer for graph to sequence learning"
    - "structural neural encoders for amr to text generation"
    - "neural data to text generation a comparison between pipeline and end to end architectures"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "enhancing amr to text generation with dual graph representations"
    - "step by step separating planning from realization in neural data to text generation"
    - "modeling graph structure in transformer for better amr to text generation"

Connected through hyperedge HE-2 (induced by source paper "discourse aware semantic self attention for narrative reading comprehension"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "self attention with relative position representations"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations"):
  with members:
    - "global to local memory pointer networks for task oriented dialogue"
    - "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "transformer xl attentive language models beyond a fixed length context"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "improving the transformer translation model with document level context"
    - "emotion detection in text a review"

Connected through hyperedge HE-4 (induced by source paper "cooperative generator discriminator networks for abstractive summarization with narrative flow"):
  with members:
    - "efficient adaptation of pretrained transformers for abstractive summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "bertscore evaluating text generation with bert"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "discourse aware neural rewards for coherent text generation"

Connected through hyperedge HE-5 (induced by source paper "graph transformer for graph to sequence learning"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "transformer xl attentive language models beyond a fixed length context"
    - "structural neural encoders for amr to text generation"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "factorising amr generation through syntax"
    - "toward abstractive summarization using semantic representations"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-6 (induced by source paper "sentence level content planning and style specification for neural text generation"):
  with members:
    - "argument generation with retrieval planning and realization"
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "shaping the narrative arc an information theoretic approach to collaborative dialogue"
    - "step by step separating planning from realization in neural data to text generation"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural argument generation augmented with externally retrieved evidence"
    - "hierarchical neural story generation"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-7 (induced by source paper "modeling graph structure in transformer for better amr to text generation"):
  with members:
    - "structural neural encoders for amr to text generation"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "factorising amr generation through syntax"
    - "self attention with relative position representations"
    - "graph to sequence learning using gated graph neural networks"
    - "a graph to sequence model for amr to text generation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "personalized patent claim generation and measurement"):
  with members:
    - "select and attend towards controllable content selection in text generation"
    - "spanbert improving pre training by representing and predicting spans"
    - "patentbert patent classification with fine tuning a pre trained bert model"
    - "scibert a pretrained language model for scientific text"
    - "docbert bert for document classification"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "defending against neural fake news"

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)