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: "free deconvolution for signal processing applications"
  Abstract: Situations in many fields of research, such as digital communications, nuclear physics and mathematical finance, can be modelled with random matrices. When the matrices get large, free probability theory is an invaluable tool for describing the asymptotic behaviour of many systems. It will be shown how free probability can be used to aid in source detection for certain systems. Sample covariance matrices for systems with noise are the starting point in our source detection problem. Multiplicative free deconvolution is shown to be a method which can aid in expressing limit eigenvalue distributions for sample covariance matrices, and to simplify estimators for eigenvalue distributions of covariance matrices.

Connected through hyperedge HE-1 (induced by source paper "convolution operations arising from vandermonde matrices"):
  with members:
    - "eigen inference for energy estimation of multiple sources"
    - "finite dimensional statistical inference"
    - "field reconstruction in sensor networks with coverage holes and packet losses"
    - "channel capacity estimation using free probability theory"
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"
    - "reconstruction of multidimensional signals from irregular noisy samples"
    - "vandermonde frequency division multiplexing for cognitive radio"

Connected through hyperedge HE-2 (induced by source paper "finite dimensional statistical inference"):
  with members:
    - "convolution operations arising from vandermonde matrices"
    - "on the optimal stacking of noisy observations"
    - "channel capacity estimation using free probability theory"

Connected through hyperedge HE-3 (induced by source paper "eigen inference for energy estimation of multiple sources"):
  with members:
    - "coverage in multi antenna two tier networks"
    - "a bayesian framework for collaborative multi source signal detection"

Connected through hyperedge HE-4 (induced by source paper "random vandermonde matrices part ii applications"):
  with members:
    - "cooperative spectrum sensing using random matrix theory"
    - "channel capacity estimation using free probability theory"
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"
    - "random vandermonde matrices part i fundamental results"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-5 (induced by source paper "on the d dimensional quasi equally spaced sampling"):
  with members:
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"
    - "reconstruction of multidimensional signals from irregular noisy samples"
    - "random vandermonde matrices part ii applications"
    - "random vandermonde matrices part i fundamental results"

Connected through hyperedge HE-6 (induced by source paper "asymptotic eigenvalue moments of wishart type random matrix without ergodicity in one channel realization"):
  with members:
    - "spectra of large block matrices"
    - "asymptotic spectral distribution of crosscorrelation matrix in asynchronous cdma"

Connected through hyperedge HE-7 (induced by source paper "random vandermonde matrices part i fundamental results"):
  with members:
    - "channel capacity estimation using free probability theory"
    - "random vandermonde matrices part ii applications"

Connected through hyperedge HE-8 (induced by source paper "cognitive ofdm network sensing a free probability approach"):
  with members:
    - "channel capacity estimation using free probability theory"

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.IT(Information Theory)