human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "raven x high performance data mining toolbox for bioacoustic data analysis"):
  Abstract: Objective of this work is to integrate high performance computing (HPC) technologies and bioacoustics data-mining capabilities by offering a MATLAB-based toolbox called Raven-X. Raven-X will provide a hardware-independent solution, for processing large acoustic datasets - the toolkit will be available to the community at no cost. This goal will be achieved by leveraging prior work done which successfully deployed MATLAB based HPC tools within Cornell University's Bioacoustics Research Program (BRP). These tools enabled commonly available multi-core computers to process data at accelerated rates to detect and classify whale sounds in large multi-channel sound archives. Through this collaboration, we will expand on this effort which was featured through Mathworks research and industry forums incorporate new cutting-edge detectors and classifiers, and disseminate Raven-X to the broader bioacoustics community.

Includes members:
  Member "high performance computer acoustic data accelerator a new system for exploring marine mammal acoustics for big data applications":
    also connected through other hyperedges:
      (none)
  Member "bioacoustical periodic pulse train signal detection and classification using spectrogram intensity binarization and energy projection":
    also connected through other hyperedges:
      - 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")
  Member "bioacoustic signal classification based on continuous region processing grid masking and artificial neural network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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")

Question: Please tell me which class the source paper 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.SD(Sound)