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 enhanced harmony search method for bangla handwritten character recognition using region sampling"
  Abstract: Identification of minimum number of local regions of a handwritten character image, containing well-defined discriminating features which are sufficient for a minimal but complete description of the character is a challenging task. A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here. The powerful framework of Harmony Search has been utilized to search the region space and detect only the most informative regions for correctly recognizing the handwritten character. The proposed method has been tested on handwritten samples of Bangla Basic, Compound and mixed (Basic and Compound characters)characters separately with SVM based classifier using a longest run based feature-set obtained from the image sub-regions formed by a CG based quad-tree partitioning approach. Applying this methodology on the above mentioned three types of datasets, respectively 43.75%, 12.5% and 37.5% gains have been achieved in terms of region reduction and 2.3%, 0.6% and 1.2% gains have been achieved in terms of recognition accuracy. The results show a sizeable reduction in the minimal number of descriptive regions as well a significant increase in recognition accuracy for all the datasets using the proposed technique. Thus the time and cost related to feature extraction is decreased without dampening the corresponding recognition accuracy.

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"
    - "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"
    - "neural architecture search with reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "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"
    - "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"
    - "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"
    - "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"
    - "an mlp based approach for recognition of handwritten bangla numerals"
    - "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)