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: "a multi biometrics for twins identification based speech and ear"
  Abstract: The development of technology biometrics becomes crucial more. To define human characteristic biometric systems are used but because of inability of traditional biometric systems to recognize twins, multimodal biometric systems are developed. In this study a multimodal biometric recognition system is proposed to recognize twins from each other and from the other people by using image and speech data. The speech or image data can be enough to recognize people from each other but twins cannot be distinguished with one of these data. Therefore a robust recognition system with the combine of speech and ear images is needed. As database, the photos and speech data of 39 twins are used. For speech recognition MFCC and DTW algorithms are used. Also, Gabor filter and DCVA algorithms are used for ear identification. Multi-biometrics success rate is increased by making matching score level fusion. Especially, rank-5 is reached 100%. We think that speech and ear can be complementary. Therefore, it is result that multi-biometrics based speech and ear is effective for human identifications.

Connected through hyperedge HE-1 (induced by source paper "twins recognition using hierarchical score level fusion"):
  with members:
    - "densely connected convolutional networks"

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)