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 "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.

Includes members:
  Member "multi task identification of entities relations and coreference for scientific knowledge graph construction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deeper task specificity improves joint entity and relation extraction")
  Member "graph to sequence learning using gated graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "depth adaptive graph recurrent network for text classification")
  Member "paper abstract writing through editing mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "describing a knowledge base")
  Member "a graph to sequence model for amr to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  Member "construction of the literature graph in semantic scholar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "navigation based candidate expansion and pretrained language models for citation recommendation")
  Member "deep graph convolutional encoders for structured data to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  Member "data to text generation with content selection and planning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "paraphrase generation with latent bag of words")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)