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 "improving reverberant speech training using diffuse acoustic simulation"):
  Abstract: We present an efficient and realistic geometric sound simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically based acoustic simulation method is capable of modeling occlusion, specular and diffuse reflections of sound in complicated acoustic environments, whereas the classical image method can only model specular reflections in simple room settings. We show that by using our synthetic training data, the same models gain significant performance improvement on real test sets in both speech recognition and keyword spotting tasks, without fine tuning using any real data.

Includes members:
  Member "scene aware audio rendering via deep acoustic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low frequency compensated synthetic impulse responses for improved far field speech recognition")
  Member "regression and classification for direction of arrival estimation with convolutional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scene aware audio rendering via deep acoustic analysis")
  Member "low frequency compensated synthetic impulse responses for improved far field speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scene aware audio rendering via deep acoustic analysis")
  Member "interactive sound rendering on mobile devices using ray parameterized reverberation filters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scene aware audio rendering via deep acoustic analysis")
  Member "permutation invariant training of deep models for speaker independent multi talker speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "joint ctc attention based end to end speech recognition using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "the microsoft 2016 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "deep clustering discriminative embeddings for segmentation and separation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")

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)