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: "low frequency compensated synthetic impulse responses for improved far field speech recognition"
  Abstract: We propose a method for generating low-frequency compensated synthetic impulse responses that improve the performance of far-field speech recognition systems trained on artificially augmented datasets. We design linear-phase filters that adapt the simulated impulse responses to equalization distributions corresponding to real-world captured impulse responses. Our filtered synthetic impulse responses are then used to augment clean speech data from LibriSpeech dataset [1]. We evaluate the performance of our method on the real-world LibriSpeech test set. In practice, our low-frequency compensated synthetic dataset can reduce the word-error-rate by up to 8.8% for far-field speech recognition.

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"
    - "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"
    - "improving reverberant speech training using diffuse acoustic simulation"
    - "scene aware audio for 360 videos"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-2 (induced by source paper "improving reverberant speech training using diffuse acoustic simulation"):
  with members:
    - "scene aware audio rendering via deep acoustic analysis"
    - "regression and classification for direction of arrival estimation with convolutional recurrent neural networks"
    - "interactive sound rendering on mobile devices using ray parameterized reverberation filters"
    - "permutation invariant training of deep models for speaker independent multi talker speech separation"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "the microsoft 2016 conversational speech recognition system"
    - "deep clustering discriminative embeddings for segmentation and separation"

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)