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 basic and compound character recognition using mlp and svm classifier"
  Abstract: A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large varieties of handwritten characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision are inherent in handwritten script. Moreover, such a large varieties of complex shaped characters, some of which have close resemblance, makes the problem of OCR of handwritten Bangla characters more difficult. Considering the complexity of the problem, the present approach makes an attempt to identify compound character classes from most frequently to less frequently occurred ones, i.e., in order of importance. This is to develop a frame work for incrementally increasing the number of learned classes of compound characters from more frequently occurred ones to less frequently occurred ones along with Basic characters. On experimentation, the technique is observed produce an average recognition rate of 79.25 after three fold cross validation of data with future scope of improvement and extension.

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"
    - "an enhanced harmony search method for bangla handwritten character recognition using region sampling"

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"
    - "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"

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 enhanced harmony search method for bangla handwritten character recognition using region sampling"
    - "an mlp based approach for recognition of handwritten bangla numerals"

Connected through hyperedge HE-4 (induced by source paper "an enhanced harmony search method for bangla handwritten character recognition using region sampling"):
  with members:
    - "an improved feature descriptor for recognition of handwritten bangla alphabet"

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)