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: "em algorithms for weighted data clustering with application to audio visual scene analysis"
  Abstract: Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical properties and to the existence of maximum-likelihood estimators based on expectation-maximization (EM). In this paper we propose a new mixture model that associates a weight with each observed point. We introduce the weighted-data Gaussian mixture and we derive two EM algorithms. The first one considers a fixed weight for each observation. The second one treats each weight as a random variable following a gamma distribution. We propose a model selection method based on a minimum message length criterion, provide a weight initialization strategy, and validate the proposed algorithms by comparing them with several state of the art parametric and non-parametric clustering techniques. We also demonstrate the effectiveness and robustness of the proposed clustering technique in the presence of heterogeneous data, namely audio-visual scene analysis.

Connected through hyperedge HE-1 (induced by source paper "deepgum learning deep robust regression with a gaussian uniform mixture model"):
  with members:
    - "a comprehensive analysis of deep regression"
    - "viraliency pooling local virality"
    - "how to make an image more memorable a deep style transfer approach"
    - "robust head pose estimation based on partially latent mixture of linear regressions"
    - "learning from noisy labels with distillation"
    - "fashion landmark detection in the wild"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "robust optimization for deep regression"

Connected through hyperedge HE-2 (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"
    - "an on line variational bayesian model for multi person tracking from cluttered scenes"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-3 (induced by source paper "audio visual speaker diarization based on spatiotemporal bayesian fusion"):
  with members:
    - "co localization of audio sources in images using binaural features and locally linear regression"

Connected through hyperedge HE-4 (induced by source paper "viraliency pooling local virality"):
  with members:
    - "how to make an image more memorable a deep style transfer approach"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "deep learning the city quantifying urban perception at a global scale"
    - "understanding image virality"
    - "a neural algorithm of artistic style"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

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.CV(Computer Vision and Pattern Recognition)