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: "a geometric approach to sound source localization from time delay estimates"
  Abstract: This paper addresses the problem of sound-source localization from time-delay estimates using arbitrarily-shaped non-coplanar microphone arrays. A novel geometric formulation is proposed, together with a thorough algebraic analysis and a global optimization solver. The proposed model is thoroughly described and evaluated. The geometric analysis, stemming from the direct acoustic propagation model, leads to necessary and sufficient conditions for a set of time delays to correspond to a unique position in the source space. Such sets of time delays are referred to as feasible sets. We formally prove that every feasible set corresponds to exactly one position in the source space, whose value can be recovered using a closed-form localization mapping. Therefore we seek for the optimal feasible set of time delays given, as input, the received microphone signals. This time delay estimation problem is naturally cast into a programming task, constrained by the feasibility conditions derived from the geometric analysis. A global branch-and-bound optimization technique is proposed to solve the problem at hand, hence estimating the best set of feasible time delays and, subsequently, localizing the sound source. Extensive experiments with both simulated and real data are reported; we compare our methodology to four state-of-the-art techniques. This comparison shows that the proposed method combined with the branch-and-bound algorithm outperforms existing methods. These in-depth geometric understanding, practical algorithms, and encouraging results, open several opportunities for future work.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "indoor sound source localization with probabilistic neural network"):
  with members:
    - "semi supervised sound source localization based on manifold regularization"

Connected through hyperedge HE-3 (induced by source paper "source localization and denoising a perspective from the tdoa space"):
  with members:
    - "tdoabased localization in two dimensions the bifurcation curve"

Connected through hyperedge HE-4 (induced by source paper "on the statistical model of source localization based on range difference measurements"):
  with members:
    - "source localization and denoising a perspective from the tdoa space"
    - "a geometrical statistical approach to outlier removal for tdoa measurements"
    - "tdoa matrices algebraic properties and their application to robust denoising with missing data"
    - "tdoabased localization in two dimensions the bifurcation curve"

Connected through hyperedge HE-5 (induced by source paper "tdoa matrices algebraic properties and their application to robust denoising with missing data"):
  with members:
    - "source localization and denoising a perspective from the tdoa space"
    - "tdoa denoising for acoustic source localization"
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "salsa a novel dataset for multimodal group behavior analysis"):
  with members:
    - "f formation detection individuating free standing conversational groups in images"
    - "vision guided robot hearing"

Connected through hyperedge HE-7 (induced by source paper "tdoa denoising for acoustic source localization"):
  with members:
    - "tdoabased localization in two dimensions the bifurcation curve"

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)