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: "finite dimensional statistical inference"
  Abstract: In this paper, we derive the explicit series expansion of the eigenvalue distribution of various models, namely the case of noncentral Wishart distributions, as well as correlated zero mean Wishart distributions. The tools used extend those of the free probability framework, which have been quite successful for high dimensional statistical inference (when the size of the matrices tends to infinity), also known as free deconvolution. This contribution focuses on the finite Gaussian case and proposes algorithmic methods to compute the moments. Cases where asymptotic results fail to apply are also discussed.

Connected through hyperedge HE-1 (induced by source paper "signal processing in large systems a new paradigm"):
  with members:
    - "random beamforming over quasi static and fading channels a deterministic equivalent approach"
    - "improved subspace estimation for multivariate observations of high dimension the deterministic signals case"
    - "eigen inference for energy estimation of multiple sources"
    - "random beamforming over correlated fading channels"
    - "a deterministic equivalent for the analysis of correlated mimo multiple access channels"
    - "on the capacity achieving covariance matrix for frequency selective mimo channels using the asymptotic approach"
    - "cooperative spectrum sensing using random matrix theory"
    - "a bayesian framework for collaborative multi source signal detection"

Connected through hyperedge HE-2 (induced by source paper "convolution operations arising from vandermonde matrices"):
  with members:
    - "eigen inference for energy estimation of multiple sources"
    - "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"
    - "free deconvolution for signal processing applications"

Connected through hyperedge HE-3 (induced by source paper "on the optimal stacking of noisy observations"):
  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)