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 "pioneer dataset and automatic recognition of urdu handwritten characters using a deep autoencoder and convolutional neural network"):
  Abstract: Automatic recognition of Urdu handwritten digits and characters, is a challenging task. It has applications in postal address reading, bank’s cheque processing, and digitization and preservation of handwritten manuscripts from old ages. While there exists a significant work for automatic recognition of handwritten English characters and other major languages of the world, the work done for Urdu language is extremely insufficient. This paper has two goals. Firstly, we introduce a pioneer dataset for handwritten digits and characters of Urdu, containing samples from more than 900 individuals. Secondly, we report results for automatic recognition of handwritten digits and characters as achieved by using deep auto-encoder network and convolutional neural network. More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy. The proposed framework of deep autoencoder can successfully recognize digits and characters with an accuracy of 97% for digits only, 81% for characters only and 82% for both digits and characters simultaneously. In comparison, the framework of convolutional neural network has accuracy of 96.7% for digits only, 86.5% for characters only and 82.7% for both digits and characters simultaneously. These frameworks can serve as baselines for future research on Urdu handwritten text.

Includes members:
  Member "building efficient cnn architecture for offline handwritten chinese character recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "template instance loss for offline handwritten chinese character recognition")
  Member "generative adversarial network for medical images mi gan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "seizure prediction using bidirectional lstm")
  Member "building fast and compact convolutional neural networks for offline handwritten chinese character recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint architecture and knowledge distillation in convolutional neural network for offline handwritten chinese text recognition")
  Member "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a limited size ensemble of homogeneous cnn lstms for high performance word classification")
  Member "convolutional neural networks applied to house numbers digit classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional networks with dense connectivity")

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.CV(Computer Vision and Pattern Recognition)