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 "attend copy parse end to end information extraction from documents"):
  Abstract: Document information extraction tasks performed by humans create data consisting of a PDF or document image input, and extracted string outputs. This end-to-end data is naturally consumed and produced when performing the task because it is valuable in and of itself. It is naturally available, at no additional cost. Unfortunately, state-of-the-art word classification methods for information extraction cannot use this data, instead requiring word-level labels which are expensive to create and consequently not available for many real life tasks. In this paper we propose the Attend, Copy, Parse architecture, a deep neural network model that can be trained directly on end-to-end data, bypassing the need for word-level labels. We evaluate the proposed architecture on a large diverse set of invoices, and outperform a state-of-the-art production system based on word classification. We believe our proposed architecture can be used on many real life information extraction tasks where word classification cannot be used due to a lack of the required word-level labels.

Includes members:
  Member "rosetta large scale system for text detection and recognition in images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep multi modal sets")
  Member "fully convolutional neural networks for page segmentation of historical document images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "multi task learning using uncertainty to weigh losses for scene geometry and semantics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatial sampling network for fast scene understanding")
  Member "cloudscan a configuration free invoice analysis system using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph convolution for multimodal information extraction from visually rich documents")
  Member "get to the point summarization with pointer generator networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "one shot learning with memory augmented neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incremental few shot object detection")
  Member "end to end sequence labeling via bi directional lstm cnns crf":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")

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)