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: "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.

Connected through hyperedge HE-1 (induced by source paper "scene aware audio rendering via deep acoustic analysis"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation"
    - "regression and classification for direction of arrival estimation with convolutional recurrent neural networks"
    - "deeplight learning illumination for unconstrained mobile mixed reality"
    - "low frequency compensated synthetic impulse responses for improved far field speech recognition"
    - "improving reverberant speech training using diffuse acoustic simulation"
    - "scene aware audio for 360 videos"
    - "codeslam learning a compact optimisable representation for dense visual slam"

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)