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 "data augmentation and deep convolutional neural networks for blind room acoustic parameter estimation"):
  Abstract: Reverberation time (T60) and the direct-to-reverberant ratio (DRR) are two commonly used parameters to characterize acoustic environments. Both parameters are useful for various speech processing applications and can be measured from an acoustic impulse response (AIR). In many scenarios, however, AIRs are not available, motivating blind estimation methods that operate directly from recorded speech. While many methods exist to solve this problem, neural networks are an appealing approach. Such methods, however, require large, balanced amounts of realistic training data (i.e. AIRs), which is expensive and time consuming to collect. To address this problem, we propose an AIR augmentation procedure that can parametrically control the T60 and DRR of real AIRs, allowing us to expand a small dataset of real AIRs into a large balanced dataset that is orders of magnitude larger. To show the validity of the method, we train a baseline convolutional neural network to predict both T60 and DDR from speech convolved with our augmented AIRs. We compare the performance of our estimators to prior work via the ACE Challenge evaluation tools and benchmarked results. Results suggest our baseline estimators outperform past single- and multi-channel state-of-the-art T60 and DRR algorithms in terms of the Pearson correlation coefficient and bias, and are either better or comparable in terms of MSE.

Includes members:
  Member "joint estimation of reverberation time and direct to reverberation ratio from speech using auditory inspired features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation")
  Member "psd estimation in beamspace for estimating direct to reverberant ratio from a reverberant speech signal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation")
  Member "single channel maximum likelihood t60 estimation exploiting subband information":
    also connected through other hyperedges:
      (none)
  Member "evaluating the non intrusive room acoustics algorithm with the ace challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)