human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs"):
  Abstract: Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are connected. In contrast, local node encoding considers the relations between directly connected nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather the best of both encoding strategies, proposing novel models that encode an input graph combining both global and local node contexts. Our approaches are able to learn better contextualized node embeddings for text generation. In our experiments, we demonstrate that our models lead to significant improvements in KG-to-text generation, achieving BLEU scores of 17.81 on AGENDA dataset, and 63.10 on the WebNLG dataset for seen categories, outperforming the state of the art by 3.51 and 2.51 points, respectively.

Includes members:
  Member "text generation from knowledge graphs with graph transformers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discourse aware semantic self attention for narrative reading comprehension")
  Member "understanding the representation power of graph neural networks in learning graph topology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalization and representational limits of graph neural networks")
  Member "graph transformer for graph to sequence learning":
    also connected through other hyperedges:
      (none)
  Member "structural neural encoders for amr to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "amr similarity metrics from principles")
  Member "neural data to text generation a comparison between pipeline and end to end architectures":
    also connected through other hyperedges:
      (none)
  Member "densely connected graph convolutional networks for graph to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "depth adaptive graph recurrent network for text classification")
  Member "enhancing amr to text generation with dual graph representations":
    also connected through other hyperedges:
      (none)
  Member "step by step separating planning from realization in neural data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "paraphrase generation with latent bag of words")

Question: Please tell me which class the source paper 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)