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: "classification for big dataset of bioacoustic signals based on human scoring system and artificial neural network"
  Abstract: In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. The results show a significant improvement in performance of the detection-classification system; yielding as much as 20% improvement in true positive rate for a given false positive rate.

Connected through hyperedge HE-1 (induced by source paper "phase 3 dcl system using deep learning approaches for land based or ship based real time recognition and localization of marine mammals bioacoustic applicaitons"):
  with members:
    - "bioacoustical periodic pulse train signal detection and classification using spectrogram intensity binarization and energy projection"
    - "bioacoustic signal classification based on continuous region processing grid masking and artificial neural network"

Connected through hyperedge HE-2 (induced by source paper "phase 4 dcl system using deep learning approaches for land based or ship based real time recognition and localization of marine mammals distributed processing and big data applications"):
  with members:
    - "bioacoustical periodic pulse train signal detection and classification using spectrogram intensity binarization and energy projection"

Connected through hyperedge HE-3 (induced by source paper "high performance computer acoustic data accelerator a new system for exploring marine mammal acoustics for big data applications"):
  with members:
    - "bioacoustical periodic pulse train signal detection and classification using spectrogram intensity binarization and energy projection"
    - "bioacoustic signal classification based on continuous region processing grid masking and artificial neural network"

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)