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: "scene aware audio rendering via deep acoustic analysis"
  Abstract: We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sources with virtual models. Given the captured audio and an approximate geometric model of a real-world room, we present a novel learning-based method to estimate its acoustic material properties. Our approach is based on deep neural networks that estimate the reverberation time and equalization of the room from recorded audio. These estimates are used to compute material properties related to room reverberation using a novel material optimization objective. We use the estimated acoustic material characteristics for audio rendering using interactive geometric sound propagation and highlight the performance on many real-world scenarios. We also perform a user study to evaluate the perceptual similarity between the recorded sounds and our rendered audio.

Connected through hyperedge HE-1 (induced by source paper "improving reverberant speech training using diffuse acoustic simulation"):
  with members:
    - "regression and classification for direction of arrival estimation with convolutional recurrent neural networks"
    - "low frequency compensated synthetic impulse responses for improved far field speech recognition"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "low frequency compensated synthetic impulse responses for improved far field speech recognition"):
  with members:
    - "regression and classification for direction of arrival estimation with convolutional recurrent neural networks"
    - "improving reverberant speech training using diffuse acoustic simulation"
    - "interactive sound rendering on mobile devices using ray parameterized reverberation filters"

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)