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: "deep graph convolutional encoders for structured data to text generation"
  Abstract: Most previous work on neural text generation from graph-structured data relies on standard sequence-to-sequence methods. These approaches linearise the input graph to be fed to a recurrent neural network. In this paper, we propose an alternative encoder based on graph convolutional networks that directly exploits the input structure. We report results on two graph-to-sequence datasets that empirically show the benefits of explicitly encoding the input graph structure.

Connected through hyperedge HE-1 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "sticking to the facts confident decoding for faithful data to text generation"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "data to text generation with entity modeling"
    - "text generation with exemplar based adaptive decoding"
    - "handling divergent reference texts when evaluating table to text generation"
    - "calibration entropy rates and memory in language models"
    - "leveraging pre trained checkpoints for sequence generation tasks"
    - "bottom up abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "densely connected graph convolutional networks for graph to sequence learning"):
  with members:
    - "structural neural encoders for amr to text generation"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph to sequence learning using gated graph neural networks"
    - "deep contextualized word representations"
    - "a graph to sequence model for amr to text generation"
    - "sentence state lstm for text representation"

Connected through hyperedge HE-4 (induced by source paper "neural language priors"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "improving tree lstm with tree attention"
    - "no training required exploring random encoders for sentence classification"
    - "universal transformers"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "universal sentence encoder"

Connected through hyperedge HE-5 (induced by source paper "text generation from knowledge graphs with graph transformers"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "graph to sequence learning using gated graph neural networks"
    - "paper abstract writing through editing mechanism"
    - "a graph to sequence model for amr to text generation"
    - "construction of the literature graph in semantic scholar"
    - "data to text generation with content selection and planning"
    - "attention is all you need"
    - "neural amr sequence to sequence models for parsing and generation"

Connected through hyperedge HE-6 (induced by source paper "effective modeling of encoder decoder architecture for joint entity and relation extraction"):
  with members:
    - "distant supervision relation extraction with intra bag and inter bag attentions"
    - "effective attention modeling for neural relation extraction"
    - "end to end neural relation extraction using deep biaffine attention"
    - "attention guided graph convolutional networks for relation extraction"
    - "a question focused multi factor attention network for question answering"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "a hierarchical framework for relation extraction with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "structural neural encoders for amr to text generation"):
  with members:
    - "graph to sequence learning using gated graph neural networks"
    - "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"
    - "graph convolutional networks for named entity recognition"
    - "opennmt open source toolkit for neural machine translation"
    - "neural amr sequence to sequence models for parsing and generation"
    - "improved neural machine translation with a syntax aware encoder and decoder"

Connected through hyperedge HE-8 (induced by source paper "generating summaries with topic templates and structured convolutional decoders"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "bootstrapping generators from noisy data"
    - "generating wikipedia by summarizing long sequences"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "data to text generation with content selection and planning"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"

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)