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: "joint recognition of handwritten text and named entities with a neural end to end model"
  Abstract: When extracting information from handwritten documents, text transcription and named entity recognition are usually faced as separate subsequent tasks. This has the disadvantage that errors in the first module affect heavily the performance of the second module. In this work we propose to do both tasks jointly, using a single neural network with a common architecture used for plain text recognition. Experimentally, the work has been tested on a collection of historical marriage records. Results of experiments are presented to show the effect on the performance for different configurations: different ways of encoding the information, doing or not transfer learning and processing at text line or multi-line region level. The results are comparable to state of the art reported in the ICDAR 2017 Information Extraction competition, even though the proposed technique does not use any dictionaries, language modeling or post processing.

Connected through hyperedge HE-1 (induced by source paper "treynet a neural model for text localization transcription and named entity recognition in full pages"):
  with members:
    - "attend copy parse end to end information extraction from documents"
    - "focal loss for dense object detection"
    - "an overview of multi task learning in deep neural networks"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "neural architectures for named entity recognition"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "no padding please efficient neural handwriting recognition"):
  with members:
    - "stronger baselines for trustable results in neural machine translation"
    - "pixel recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "cells in multidimensional recurrent neural networks"
    - "dropout improves recurrent neural networks for handwriting recognition"
    - "on the difficulty of training recurrent neural networks"
    - "multi dimensional recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "bench marking information extraction in semi structured historical handwritten records"):
  with members:
    - "system description of citlab s recognition retrieval engine for icdar2017 competition on information extraction in historical handwritten records"
    - "neural architectures for named entity recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "citlab argus for historical handwritten documents"
    - "natural language processing almost from scratch"

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.CV(Computer Vision and Pattern Recognition)