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: "deep learning for word level handwritten indic script identification"
  Abstract: We propose a novel method that uses convolutional neural networks (CNNs) for feature extraction. Not just limited to conventional spatial domain representation, we use multilevel 2D discrete Haar wavelet transform, where image representations are scaled to a variety of different sizes. These are then used to train different CNNs to select features. To be precise, we use 10 different CNNs that select a set of 10240 features, i.e. 1024/CNN. With this, 11 different handwritten scripts are identified, where 1K words per script are used. In our test, we have achieved the maximum script identification rate of 94.73% using multi-layer perceptron (MLP). Our results outperform the state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "handwritten indic character recognition using capsule networks"):
  with members:
    - "dynamic routing between capsules"
    - "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"
    - "an enhanced harmony search method for bangla handwritten character recognition using region sampling"
    - "an mlp based approach for recognition of handwritten bangla numerals"
    - "handwritten bangla basic and compound character recognition using mlp and svm classifier"

Connected through hyperedge HE-2 (induced by source paper "indic handwritten script identification using offline online multimodal deep network"):
  with members:
    - "handwriting trajectory recovery using end to end deep encoder decoder network"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "hmm based writer identification in music score documents without staff line removal"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "hmm based indic handwritten word recognition using zone segmentation"
    - "local binary pattern for word spotting in handwritten historical document"
    - "compact bilinear pooling"

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)