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: "bioacoustic signal classification based on continuous region processing grid masking and artificial neural network"
  Abstract: In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. The method is evaluated on a dataset recorded in Massachusetts Bay, United States. The dataset includes 20000 sound clips for training, and 10000 sound clips for testing. The results show that the proposed technique can achieve an error rate of less than FPR = 4.5% for a 90% true 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"
    - "classification for big dataset of bioacoustic signals based on human scoring system and artificial neural network"

Connected through hyperedge HE-2 (induced by source paper "raven x high performance data mining toolbox for bioacoustic data analysis"):
  with members:
    - "high performance computer acoustic data accelerator a new system for exploring marine mammal acoustics for big data applications"
    - "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"
    - "classification for big dataset of bioacoustic signals based on human scoring system 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)