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: "acoustic space learning for sound source separation and localization on binaural manifolds"
  Abstract: In this paper we address the problems of modeling the acoustic space generated by a full-spectrum sound source and of using the learned model for the localization and separation of multiple sources that simultaneously emit sparse-spectrum sounds. We lay theoretical and methodological grounds in order to introduce the binaural manifold paradigm. We perform an in-depth study of the latent low-dimensional structure of the high-dimensional interaural spectral data, based on a corpus recorded with a human-like audiomotor robot head. A non-linear dimensionality reduction technique is used to show that these data lie on a two-dimensional (2D) smooth manifold parameterized by the motor states of the listener, or equivalently, the sound source directions. We propose a probabilistic piecewise affine mapping model (PPAM) specifically designed to deal with high-dimensional data exhibiting an intrinsic piecewise linear structure. We derive a closed-form expectation-maximization (EM) procedure for estimating the model parameters, followed by Bayes inversion for obtaining the full posterior density function of a sound source direction. We extend this solution to deal with missing data and redundancy in real world spectrograms, and hence for 2D localization of natural sound sources such as speech. We further generalize the model to the challenging case of multiple sound sources and we propose a variational EM framework. The associated algorithm, referred to as variational EM for source separation and localization (VESSL) yields a Bayesian estimation of the 2D locations and time-frequency masks of all the sources. Comparisons of the proposed approach with several existing methods reveal that the combination of acoustic-space learning with Bayesian inference enables our method to outperform state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "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"
    - "supervised and unsupervised speech enhancement using nonnegative matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "relative transfer function inverse regression from low dimensional manifold"):
  with members:
    - "the fifth chime speech separation and recognition challenge dataset task and baselines"
    - "semi supervised source localization on multiple manifolds with distributed microphones"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "spatial source subtraction based on incomplete measurements of relative transfer function"
    - "semi supervised sound source localization based on manifold regularization"
    - "high dimensional regression with gaussian mixtures and partially latent response variables"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "towards a generalization of relative transfer functions to more than one source"
    - "semi supervised sound source localization based on manifold regularization"

Connected through hyperedge HE-4 (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"
    - "towards a generalization of relative transfer functions to more than one source"
    - "semi supervised sound source localization based on manifold regularization"
    - "robust sound source localization using a microphone array on a mobile robot"

Connected through hyperedge HE-5 (induced by source paper "vast the virtual acoustic space traveler dataset"):
  with members:
    - "hearing in a shoe box binaural source position and wall absorption estimation using virtually supervised learning"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "high dimensional regression with gaussian mixtures and partially latent response variables"

Connected through hyperedge HE-6 (induced by source paper "estimation of the direct path relative transfer function for supervised sound source localization"):
  with members:
    - "binaural coherent to diffuse ratio estimation for dereverberation using an itd model"
    - "co localization of audio sources in images using binaural features and locally linear regression"
    - "coherent to diffuse power ratio estimation for dereverberation"

Connected through hyperedge HE-7 (induced by source paper "rectified binaural ratio a complex t distributed feature for robust sound localization"):
  with members:
    - "towards a generalization of relative transfer functions to more than one source"

Connected through hyperedge HE-8 (induced by source paper "co localization of audio sources in images using binaural features and locally linear regression"):
  with members:
    - "high dimensional regression with gaussian mixtures and partially latent response variables"

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)