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 bangla alphabet recognition using an mlp based classifier"
  Abstract: The work presented here involves the design of a Multi Layer Perceptron (MLP) based classifier for recognition of handwritten Bangla alphabet using a 76 element feature set Bangla is the second most popular script and language in the Indian subcontinent and the fifth most popular language in the world. The feature set developed for representing handwritten characters of Bangla alphabet includes 24 shadow features, 16 centroid features and 36 longest-run features. Recognition performances of the MLP designed to work with this feature set are experimentally observed as 86.46% and 75.05% on the samples of the training and the test sets respectively. The work has useful application in the development of a complete OCR system for handwritten Bangla text.

Connected through hyperedge HE-1 (induced by source paper "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "optimizing memory efficiency for deep convolutional neural networks on gpus"
    - "theano a python framework for fast computation of mathematical expressions"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-2 (induced by source paper "recognition of handwritten bangla basic characters and digits using convex hull based feature set"):
  with members:
    - "a ga based approach for selection of local features for recognition of handwritten bangla numerals"
    - "design of a novel convex hull based feature set for recognition of isolated handwritten roman numerals"

Connected through hyperedge HE-3 (induced by source paper "non correlated character recognition using artificial neural network"):
  with members:
    - "time efficient approach to offline hand written character recognition using associative memory net"
    - "neural networks for handwritten english alphabet recognition"

Connected through hyperedge HE-4 (induced by source paper "english character recognition using artificial neural network"):
  with members:
    - "neural networks for handwritten english alphabet recognition"

Connected through hyperedge HE-5 (induced by source paper "time efficient approach to offline hand written character recognition using associative memory net"):
  with members:
    - "neural networks for handwritten english alphabet recognition"

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)