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: "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation"
  Abstract: The reverberation time (T60) and the direct-to-reverberant ratio (DRR) are commonly used to characterize room acoustic environments. Both parameters can be measured from an acoustic impulse response (AIR) or using blind estimation methods that perform estimation directly from speech. When neural networks are used for blind estimation, however, a large realistic dataset is needed, which is expensive and time consuming to collect. To address this, we propose an AIR augmentation method that can parametrically control the T60 and DRR, allowing us to expand a small dataset of real AIRs into a balanced dataset orders of magnitude larger. Using this method, we train a previously proposed convolutional neural network (CNN) and show we can outperform past single-channel state-of-the-art methods. We then propose a more efficient, straightforward baseline CNN that is 4-5x faster, which provides an additional improvement and is better or comparable to all previously reported single- and multi-channel state-of-the-art methods.

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"
    - "data augmentation and deep convolutional 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)