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: "source localization and denoising a perspective from the tdoa space"
  Abstract: In this manuscript, we formulate the problem of source localization based on Time Differences of Arrival (TDOAs) in the TDOA space, i.e., the Euclidean space spanned by TDOA measurements. More specifically, we show that source localization can be interpreted as a denoising problem of TDOA measurements. As this denoising problem is difficult to solve in general, our analysis shows that it is possible to resort to a relaxed version of it. The solution of the relaxed problem through linear operations in the TDOA space is then discussed, and its analysis leads to a parallelism with other state-of-the-art TDOA denoising algorithms. Additionally, we extend the proposed solution also to the case where only TDOAs between few pairs of microphones within an array have been computed. The reported denoising algorithms are all analytically justified, and numerically tested through simulative campaign.

Connected through hyperedge HE-1 (induced by source paper "a geometrical statistical approach to outlier removal for tdoa measurements"):
  with members:
    - "on the statistical model of source localization based on range difference 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-2 (induced by source paper "on the statistical model of source localization based on range difference measurements"):
  with members:
    - "a geometrical statistical approach to outlier removal for tdoa measurements"
    - "tdoa matrices algebraic properties and their application to robust denoising with missing data"
    - "a geometric approach to sound source localization from time delay estimates"
    - "tdoabased localization in two dimensions the bifurcation curve"

Connected through hyperedge HE-3 (induced by source paper "tdoa matrices algebraic properties and their application to robust denoising with missing data"):
  with members:
    - "tdoa denoising for acoustic source localization"
    - "a geometric approach to sound source localization from time delay estimates"
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"

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)