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: "robust localization and tracking of simultaneous moving sound sources using beamforming and particle filtering"
  Abstract: Mobile robots in real-life settings would benefit from being able to localize and track sound sources. Such a capability can help localizing a person or an interesting event in the environment, and also provides enhanced processing for other capabilities such as speech recognition. To give this capability to a robot, the challenge is not only to localize simultaneous sound sources, but to track them over time. In this paper we propose a robust sound source localization and tracking method using an array of eight microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a particle filter-based tracking algorithm. Results show that a mobile robot can localize and track in real-time multiple moving sources of different types over a range of 7 m. These new capabilities allow a mobile robot to interact using more natural means with people in real-life settings.

Connected through hyperedge HE-1 (induced by source paper "high resolution speaker counting in reverberant rooms using crnn with ambisonics features"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"):
  with members:
    - "multichannel sound event detection using 3d convolutional neural networks for learning inter channel features"
    - "deep neural networks for multiple speaker detection and localization"
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "a report on sound event detection with different binaural features"
    - "convolutional recurrent neural networks for polyphonic sound event detection"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "sound event detection using spatial features and convolutional recurrent neural network"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"

Connected through hyperedge HE-3 (induced by source paper "modeling the comb filter effect and interaural coherence for binaural source separation"):
  with members:
    - "acoustic reflector localization novel image source reversion and direct localization methods"
    - "joint optimization of masks and deep recurrent neural networks for monaural source separation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "acoustic space learning for sound source separation and localization on binaural manifolds"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "raking the cocktail party"
    - "audio surveillance a systematic review"

Connected through hyperedge HE-4 (induced by source paper "localization detection and tracking of multiple moving sound sources with a convolutional recurrent neural network"):
  with members:
    - "sound event localization and detection of overlapping sources using convolutional recurrent neural networks"
    - "deep neural networks for multiple speaker detection and localization"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "audio surveillance a systematic review"

Connected through hyperedge HE-5 (induced by source paper "online localization and tracking of multiple moving speakers in reverberant environments"):
  with members:
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"

Connected through hyperedge HE-6 (induced by source paper "robust sound source localization considering similarity of back propagation signals"):
  with members:
    - "scene aware audio for 360 videos"
    - "diffraction aware sound localization for a non line of sight source"
    - "reflection aware sound source localization"

Connected through hyperedge HE-7 (induced by source paper "self supervised generation of spatial audio for 360 video"):
  with members:
    - "the conversation deep audio visual speech enhancement"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "the sound of pixels"
    - "audio visual scene analysis with self supervised multisensory features"
    - "supervised speech separation based on deep learning an overview"
    - "audio super resolution using neural networks"
    - "identity mappings in deep residual networks"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-8 (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers"):
  with members:
    - "online localization and tracking of multiple moving speakers in reverberant environments"
    - "tracking multiple audio sources with the von mises distribution and variational em"
    - "audio visual speaker diarization based on spatiotemporal bayesian fusion"
    - "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization"
    - "mot16 a benchmark for multi object tracking"
    - "person re identification in the wild"
    - "em algorithms for weighted data clustering with application to audio visual scene analysis"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"

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.RO(Robotics)