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: "towards a generalization of relative transfer functions to more than one source"
  Abstract: We propose a natural way to generalize relative transfer functions (RTFs) to more than one source. We first prove that such a generalization is not possible using a single multichannel spectro-temporal observation, regardless of the number of microphones. We then introduce a new transform for multichannel multi-frame spectrograms, i.e., containing several channels and time frames in each time-frequency bin. This transform allows a natural generalization which satisfies the three key properties of RTFs, namely, they can be directly estimated from observed signals, they capture spatial properties of the sources and they do not depend on emitted signals. Through simulated experiments, we show how this new method can localize multiple simultaneously active sound sources using short spectro-temporal windows, without relying on source separation.

Connected through hyperedge HE-1 (induced by source paper "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"):
  with members:
    - "semi supervised source localization on multiple manifolds with distributed microphones"
    - "rectified binaural ratio a complex t distributed feature for robust sound localization"
    - "estimation of the direct path relative transfer function for supervised sound source localization"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "semi supervised sound source localization based on manifold regularization"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"

Connected through hyperedge HE-2 (induced by source paper "semi supervised source localization on multiple manifolds with distributed microphones"):
  with members:
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "semi supervised sound source localization based on manifold regularization"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "robust sound source localization using a microphone array on a mobile robot"

Connected through hyperedge HE-3 (induced by source paper "rectified binaural ratio a complex t distributed feature for robust sound localization"):
  with members:
    - "acoustic space learning for sound source separation and localization on binaural manifolds"

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)