human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "variational bayesian inference for audio visual tracking of multiple speakers"):
  Abstract: In this paper we address the problem of tracking multiple speakers via the fusion of visual and auditory information. We propose to exploit the complementary nature of these two modalities in order to accurately estimate smooth trajectories of the tracked persons, to deal with the partial or total absence of one of the modalities over short periods of time, and to estimate the acoustic status -- either speaking or silent -- of each tracked person along time. We propose to cast the problem at hand into a generative audio-visual fusion (or association) model formulated as a latent-variable temporal graphical model. This may well be viewed as the problem of maximizing the posterior joint distribution of a set of continuous and discrete latent variables given the past and current observations, which is intractable. We propose a variational inference model which amounts to approximate the joint distribution with a factorized distribution. The solution takes the form of a closed-form expectation maximization procedure. We describe in detail the inference algorithm, we evaluate its performance and we compare it with several baseline methods. These experiments show that the proposed audio-visual tracker performs well in informal meetings involving a time-varying number of people.

Includes members:
  Member "online localization and tracking of multiple moving speakers in reverberant environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "high resolution speaker counting in reverberant rooms using crnn with ambisonics features")
  Member "tracking multiple audio sources with the von mises distribution and variational em":
    also connected through other hyperedges:
      (none)
  Member "audio visual speaker diarization based on spatiotemporal bayesian fusion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ava activespeaker an audio visual dataset for active speaker detection")
  Member "multiple speaker localization based on direct path features and likelihood maximization with spatial sparsity regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sinusoidal wave generating network based on adversarial learning and its application synthesizing frog sounds for data augmentation")
  Member "mot16 a benchmark for multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "person re identification in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "em algorithms for weighted data clustering with application to audio visual scene analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepgum learning deep robust regression with a gaussian uniform mixture model")
  Member "an on line variational bayesian model for multi person tracking from cluttered scenes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "how to train your deep multi object tracker")

Question: Please tell me which class the source paper 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)