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: "independent vector analysis identification conditions and performance bounds"
  Abstract: Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been a subject of significant research interest. IVA has also been shown to be a generalization of Hotelling's canonical correlation analysis. In this paper, we provide the identification conditions for a general IVA formulation, which accounts for linear, nonlinear, and sample-to-sample dependencies. The identification conditions are a generalization of previous results for ICA and for IVA when samples are independently and identically distributed. Furthermore, a principal aim of IVA is identification of dependent sources between datasets. Thus, we provide additional conditions for when the arbitrary ordering of the estimated sources can be common across datasets. Performance bounds in terms of the Cramer-Rao lower bound are also provided for demixing matrices and interference to source ratio. The performance of two IVA algorithms are compared to the theoretical bounds.

Connected through hyperedge HE-1 (induced by source paper "determining the dimension and structure of the subspace correlated across multiple data sets"):
  with members:
    - "canonical correlation analysis of high dimensional data with very small sample support"

Connected through hyperedge HE-2 (induced by source paper "performance analysis of source image estimators in blind source separation"):
  with members:
    - "spatial source subtraction based on incomplete measurements of relative transfer function"

Connected through hyperedge HE-3 (induced by source paper "linked component analysis from matrices to high order tensors applications to biomedical data"):
  with members:
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "smooth parafac decomposition for tensor completion"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "robust principal component analysis on graphs"
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "tensor networks for big data analytics and large scale optimization problems"

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.LG(Machine Learning)