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 "relative transfer function inverse regression from low dimensional manifold"):
  Abstract: In room acoustic environments, the Relative Transfer Functions (RTFs) are controlled by few underlying modes of variability. Accordingly, they are confined to a low-dimensional manifold. In this letter, we investigate a RTF inverse regression problem, the task of which is to generate the high-dimensional responses from their low-dimensional representations. The problem is addressed from a pure data-driven perspective and a supervised Deep Neural Network (DNN) model is applied to learn a mapping from the source-receiver poses (positions and orientations) to the frequency domain RTF vectors. The experiments show promising results: the model achieves lower prediction error of the RTF than the free field assumption. However, it fails to compete with the linear interpolation technique in small sampling distances.

Includes members:
  Member "the fifth chime speech separation and recognition challenge dataset task and baselines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on loss functions for supervised monaural time domain speech enhancement")
  Member "semi supervised source localization on multiple manifolds with distributed microphones":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi source doa estimation through pattern recognition of the modal coherence of a reverberant soundfield")
  Member "co localization of audio sources in images using binaural features and locally linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers")
  Member "spatial source subtraction based on incomplete measurements of relative transfer function":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block online multi channel speech enhancement using dnn supported relative transfer function estimates")
  Member "semi supervised sound source localization based on manifold regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "indoor sound source localization with probabilistic neural network")
  Member "high dimensional regression with gaussian mixtures and partially latent response variables":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers")
  Member "acoustic space learning for sound source separation and localization on binaural manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling the comb filter effect and interaural coherence for binaural source separation")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)