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: "an mlp based approach for recognition of handwritten bangla numerals"
  Abstract: The work presented here involves the design of a Multi Layer Perceptron (MLP) based pattern classifier for recognition of handwritten Bangla digits using a 76 element feature vector. 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 Bangla numerals here includes 24 shadow features, 16 centroid features and 36 longest-run features. On experimentation with a database of 6000 samples, the technique yields an average recognition rate of 96.67% evaluated after three-fold cross validation of results. It is useful for applications related to OCR of handwritten Bangla Digit and can also be extended to include OCR of handwritten characters of Bangla alphabet.

Connected through hyperedge HE-1 (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"
    - "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"
    - "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"
    - "handwritten bangla basic and compound character recognition using mlp and svm classifier"

Connected through hyperedge HE-2 (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"
    - "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"
    - "handwritten bangla basic and compound character recognition using mlp and svm classifier"

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)