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: "handwritten arabic numeral recognition using deep learning neural networks"
  Abstract: Handwritten character recognition is an active area of research with applications in numerous fields. Past and recent works in this field have concentrated on various languages. Arabic is one language where the scope of research is still widespread, with it being one of the most popular languages in the world and being syntactically different from other major languages. Das et al. \cite{DBLP:journals/corr/abs-1003-1891} has pioneered the research for handwritten digit recognition in Arabic. In this paper, we propose a novel algorithm based on deep learning neural networks using appropriate activation function and regularization layer, which shows significantly improved accuracy compared to the existing Arabic numeral recognition methods. The proposed model gives 97.4 percent accuracy, which is the recorded highest accuracy of the dataset used in the experiment. We also propose a modification of the method described in \cite{DBLP:journals/corr/abs-1003-1891}, where our method scores identical accuracy as that of \cite{DBLP:journals/corr/abs-1003-1891}, with the value of 93.8 percent.

Connected through hyperedge HE-1 (induced by source paper "a novel transfer learning approach upon hindi arabic and bangla numerals using convolutional neural networks"):
  with members:
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-2 (induced by source paper "reduction of overfitting in diabetes prediction using deep learning neural network"):
  with members:
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "applying data augmentation to handwritten arabic numeral recognition using deep learning neural networks"):
  with members:
    - "a novel transfer learning approach upon hindi arabic and bangla numerals using convolutional neural networks"
    - "reduction of overfitting in diabetes prediction using deep learning neural network"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adadelta an adaptive learning rate method"
    - "handwritten arabic numeral recognition using a multi layer perceptron"

Connected through hyperedge HE-4 (induced by source paper "an efficient single chord based accumulation technique sca to detect more reliable corners"):
  with members:
    - "a novel transfer learning approach upon hindi arabic and bangla numerals using convolutional neural networks"
    - "applying data augmentation to handwritten arabic numeral recognition using deep learning neural networks"
    - "chord angle deviation using tangent cadt an efficient and robust contour based corner detector"

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)