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: "brachiaria species identification using imaging techniques based on fractal descriptors"
  Abstract: Abstract   The use of a rapid and accurate method in diagnosis and classification of species and/or cultivars of forage has practical relevance, scientific and trade in various areas of study, since it has broad representation in grazing from tropical regions. Nowadays it occupies about 90% of the grazing area along Brazil and, besides the grazing areas to feed ruminants,  Brachiaria  also corresponds to about 80% of seeds being traded in all the world, bringing a large amount of money to Brazil. To identify species and/or cultivars of this genus is of fundamental importance in the fields that produce seeds, to ensure varietal purity and the effectiveness of improvement programs. Thus, leaf samples of fodder plant species  Brachiaria  were previously identified, collected and scanned to be treated by means of artificial vision to make the database and be used in subsequent classifications. Forage crops used were:  Brachiaria decumbens  cv. IPEAN;  Brachiaria ruziziensis  Germain & Evrard;  Brachiaria brizantha  (Hochst. ex. A. Rich.) Stapf;  Brachiaria arrecta  (Hack.) Stent. and  Brachiaria  spp. The images were analyzed by the fractal descriptors method, where a set of measures are obtained from the values of the fractal dimension at different scales. Therefore such values are used as inputs for a state-of-the-art classifier, the Support Vector Machine, which finally discriminates the images according to the respective species. The proposed method outperforms other state-of-the-art image analysis methods and makes possible the correct prediction of species in more than 93% of the samples. Such remarkable result is consequence of the better suitability of representing complex structures like those arising in the plant leaves by measures of complexity from fractal geometry. Finally, this high correctness rate suggests that the fractal method is an important tool to help the botanist.

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)