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: "funsd a dataset for form understanding in noisy scanned documents"
  Abstract: In this paper, we present a new dataset for Form Understanding in Noisy Scanned Documents (FUNSD). Form Understanding (FoUn) aims at extracting and structuring the textual content of forms. The dataset comprises 200 fully annotated real scanned forms. The documents are noisy and exhibit large variabilities in their representation making FoUn a challenging task. The proposed dataset can be used for various tasks including text detection, optical character recognition (OCR), spatial layout analysis and entity labeling/linking. To the best of our knowledge this is the first publicly available dataset with comprehensive annotations addressing the FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset. The FUNSD dataset can be downloaded at https://guillaumejaume.github. io/FUNSD/.

Connected through hyperedge HE-1 (induced by source paper "kleister a novel task for information extraction involving long documents with complex layout"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "cutie learning to understand documents with convolutional universal text information extractor"
    - "one shot information extraction from document images using neuro deductive program synthesis"
    - "roberta a robustly optimized bert pretraining approach"
    - "chargrid towards understanding 2d documents"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikireading a novel large scale language understanding task over wikipedia"

Connected through hyperedge HE-2 (induced by source paper "layoutlm pre training of text and layout for document image understanding"):
  with members:
    - "graph convolution for multimodal information extraction from visually rich documents"
    - "modular multimodal architecture for document classification"
    - "publaynet largest dataset ever for document layout analysis"
    - "roberta a robustly optimized bert pretraining approach"
    - "chargrid towards understanding 2d documents"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "ddi 100 dataset for text detection and recognition"):
  with members:
    - "east an efficient and accurate scene text detector"
    - "synthetic data for text localisation in natural images"
    - "detecting text in natural image with connectionist text proposal network"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "microsoft coco captions data collection and evaluation server"
    - "evaluation of deep convolutional nets for document image classification and retrieval"

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.IR(Information Retrieval)