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 "graph convolution for multimodal information extraction from visually rich documents"):
  Abstract: Visually rich documents (VRDs) are ubiquitous in daily business and life. Examples are purchase receipts, insurance policy documents, custom declaration forms and so on. In VRDs, visual and layout information is critical for document understanding, and texts in such documents cannot be serialized into the one-dimensional sequence without losing information. Classic information extraction models such as BiLSTM-CRF typically operate on text sequences and do not incorporate visual features. In this paper, we introduce a graph convolution based model to combine textual and visual information presented in VRDs. Graph embeddings are trained to summarize the context of a text segment in the document, and further combined with text embeddings for entity extraction. Extensive experiments have been conducted to show that our method outperforms BiLSTM-CRF baselines by significant margins, on two real-world datasets. Additionally, ablation studies are also performed to evaluate the effectiveness of each component of our model.

Includes members:
  Member "n ary relation extraction using graph state lstm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mining commonsense facts from the physical world")
  Member "chargrid towards understanding 2d documents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "graph2seq graph to sequence learning with attention based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards graph representation learning in emergent communication")
  Member "image generation from scene graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "cross sentence n ary relation extraction with graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive fractional order graph neural network")
  Member "multi task learning using uncertainty to weigh losses for scene geometry and semantics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatial sampling network for fast scene understanding")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "joint extraction of entities and relations based on a novel tagging scheme":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hybrid neural tagging model for open relation extraction")

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.IR(Information Retrieval)