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: "psd estimation in beamspace for estimating direct to reverberant ratio from a reverberant speech signal"
  Abstract: A method for estimation of direct-to-reverberant ratio (DRR) using a microphone array is proposed. The proposed method estimates the power spectral density (PSD) of the direct sound and the reverberation using the algorithm \textit{PSD estimation in beamspace} with a microphone array and calculates the DRR of the observed signal. The speech corpus of the ACE (Acoustic Characterisation of Environments) Challenge was utilised for evaluating the practical feasibility of the proposed method. The experimental results revealed that the proposed method was able to effectively estimate the DRR from a recording of a reverberant speech signal which included various environmental noise.

Connected through hyperedge HE-1 (induced by source paper "data augmentation and deep convolutional neural networks for blind room acoustic parameter estimation"):
  with members:
    - "joint estimation of reverberation time and direct to reverberation ratio from speech using auditory inspired features"
    - "single channel maximum likelihood t60 estimation exploiting subband information"
    - "evaluating the non intrusive room acoustics algorithm with the ace challenge"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation"):
  with members:
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "joint estimation of reverberation time and direct to reverberation ratio from speech using auditory inspired features"
    - "evaluating the non intrusive room acoustics algorithm with the ace challenge"
    - "adam a method for stochastic optimization"

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)