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 volumetric srp with refinement step for sound source localization"
  Abstract: This letter proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the refined volumetric SRP (RV-SRP). By deploying a sparser volumetric grid, the RV-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of location estimates. In addition, a refinement step improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios show that the RV-SRP outperforms state-of-the-art methods in accuracy with lower computational cost.

Connected through hyperedge HE-1 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "indoor sound source localization with probabilistic neural network"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "sound source localization in a multipath environment using convolutional neural networks"
    - "semi supervised source localization on multiple manifolds with distributed microphones"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "audio surveillance a systematic review"

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.SD(Sound)