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: "bioacoustical periodic pulse train signal detection and classification using spectrogram intensity binarization and energy projection"
  Abstract: The following work outlines an approach for automatic detection and recognition of periodic pulse train signals using a multi-stage process based on spectrogram edge detection, energy projection and classification. The method has been implemented to automatically detect and recognize pulse train songs of minke whales. While the long term goal of this work is to properly identify and detect minke songs from large multi-year datasets, this effort was developed using sounds off the coast of Massachusetts, in the Stellwagen Bank National Marine Sanctuary. The detection methodology is presented and evaluated on 232 continuous hours of acoustic recordings and a qualitative analysis of machine learning classifiers and their performance is described. The trained automatic detection and classification system is applied to 120 continuous hours, comprised of various challenges such as broadband and narrowband noises, low SNR, and other pulse train signatures. This automatic system achieves a TPR of 63% for FPR of 0.6% (or 0.87 FP/h), at a Precision (PPV) of 84% and an F1 score of 71%.

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:
    - "classification for big dataset of bioacoustic signals based on human scoring system and artificial neural network"
    - "bioacoustic signal classification based on continuous region processing grid masking 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"
    - "bioacoustic signal classification based on continuous region processing grid masking and artificial neural network"

Connected through hyperedge HE-3 (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:
    - "classification for big dataset of bioacoustic signals based on human scoring system and artificial neural network"

Connected through hyperedge HE-4 (induced by source paper "high performance computer acoustic data accelerator a new system for exploring marine mammal acoustics for big data applications"):
  with members:
    - "classification for big dataset of bioacoustic signals based on human scoring system and artificial neural network"
    - "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)