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 isolated bangla compound character recognition a new benchmark using a novel deep learning approach"
  Abstract: Abstract   In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmarkof recognition accuracy on the CMATERdb 3.1.3.3 dataset is reported. Greedy layer wise training of Deep Neural Network has helped to make significant strides in various pattern recognition problems. We employ layerwise training to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and augment the training process with the RMSProp algorithm to achieve faster convergence. We compare results with those obtained from standard shallow learning methods with predefined features, as well as standard DCNNs. Supervised layerwise trained DCNNs are found to outperform standard shallow learning models such as Support Vector Machines as well as regular DCNNs of similar architecture by achieving error rate of 9.67% thereby setting a new benchmark on the CMATERdb 3.1.3.3 with recognition accuracy of 90.33%, representing an improvement of nearly 10%.

Connected through hyperedge HE-1 (induced by source paper "a genetic algorithm based kernel size selection approach for a multi column convolutional neural network"):
  with members:
    - "nsga net neural architecture search using multi objective genetic algorithm"
    - "efficient neural architecture search via parameter sharing"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "progressive neural architecture search"
    - "neural architecture search with reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "an enhanced harmony search method for bangla handwritten character recognition using region sampling"
    - "roulette wheel selection via stochastic acceptance"

Connected through hyperedge HE-2 (induced by source paper "using dynamic routing to extract intermediate features for developing scalable capsule networks"):
  with members:
    - "handwritten indic character recognition using capsule networks"
    - "dynamic routing between capsules"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "an enhanced harmony search method for bangla handwritten character recognition using region sampling"
    - "going deeper with convolutions"
    - "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-3 (induced by source paper "handwritten indic character recognition using capsule networks"):
  with members:
    - "deep learning for word level handwritten indic script identification"
    - "dynamic routing between capsules"
    - "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-4 (induced by source paper "handwritten amharic character recognition using a convolutional neural network"):
  with members:
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"

Connected through hyperedge HE-5 (induced by source paper "deep learning for word level handwritten indic script identification"):
  with members:
    - "adam a method for stochastic optimization"

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)