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: "microphone array post filter for separation of simultaneous non stationary sources"
  Abstract: Microphone array post-filters have demonstrated their ability to greatly reduce noise at the output of a beamformer. However, current techniques only consider a single source of interest, most of the time assuming stationary background noise. We propose a microphone array post-filter that enhances the signals produced by the separation of simultaneous sources using common source separation algorithms. Our method is based on a loudness-domain optimal spectral estimator and on the assumption that the noise can be described as the sum of a stationary component and of a transient component that is due to leakage between the channels of the initial source separation algorithm. The system is evaluated in the context of mobile robotics and is shown to produce better results than current post-filtering techniques, greatly reducing interference while causing little distortion to the signal of interest, even at very low SNR.

Connected through hyperedge HE-1 (induced by source paper "a hybrid dsp deep learning approach to real time full band speech enhancement"):
  with members:
    - "high quality low delay music coding in the opus codec"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "multichannel linear prediction for blind reverberant audio source separation"):
  with members:
    - "enhanced robot audition based on microphone array source separation with post filter"

Connected through hyperedge HE-3 (induced by source paper "auditory system for a mobile robot"):
  with members:
    - "enhanced robot audition based on microphone array source separation with post filter"
    - "localization of simultaneous moving sound sources for mobile robot using a frequency domain steered beamformer approach"
    - "robust sound source localization using a microphone array on a mobile robot"

Connected through hyperedge HE-4 (induced by source paper "robust recognition of simultaneous speech by a mobile robot"):
  with members:
    - "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"
    - "robust 3d localization and tracking of sound sources using beamforming and particle filtering"
    - "enhanced robot audition based on microphone array source separation with post filter"
    - "localization of simultaneous moving sound sources for mobile robot using a frequency domain steered beamformer approach"

Connected through hyperedge HE-5 (induced by source paper "robust 3d localization and tracking of sound sources using beamforming and particle filtering"):
  with members:
    - "localization of simultaneous moving sound sources for mobile robot using a frequency domain steered beamformer approach"

Connected through hyperedge HE-6 (induced by source paper "enhanced robot audition based on microphone array source separation with post filter"):
  with members:
    - "localization of simultaneous moving sound sources for mobile robot using a frequency domain steered beamformer approach"

Connected through hyperedge HE-7 (induced by source paper "localization of simultaneous moving sound sources for mobile robot using a frequency domain steered beamformer approach"):
  with members:
    - "robust sound source localization using a microphone array on a mobile robot"

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)