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: "channel capacity estimation using free probability theory"
  Abstract: In many channel measurement applications, one needs to estimate some characteristics of the channels based on a limited set of measurements. This is mainly due to the highly time varying characteristics of the channel. In this paper, it will be shown how free probability can be used for channel capacity estimation in MIMO systems. Free probability has already been applied in various application fields such as digital communications, nuclear physics, and mathematical finance, and has been shown to be an invaluable tool for describing the asymptotic behavior of many large-dimensional systems. In particular, using the concept of free deconvolution, we provide an asymptotically (with respect to the number of observations) unbiased capacity estimator for MIMO channels impaired with noise called the free probability based estimator. Another estimator, called the Gaussian matrix-mean-based estimator, is also introduced by slightly modifying the free-probability-based estimator. This estimator is shown to give unbiased estimation of the moments of the channel matrix for any number of observations. Also, the estimator has this property when we extend to MIMO channels with phase off-set and frequency drift, for which no estimator has been provided so far in the literature. It is also shown that both the free-probability-based and the Gaussian matrix-mean-based estimator are asymptotically unbiased capacity estimators as the number of transmit antennas go to infinity, regardless of whether phase off-set and frequency drift are present. The limitations in the two estimators are also explained. Simulations are run to assess the performance of the estimators for a low number of antennas and samples to confirm the usefulness of the asymptotic results.

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"
    - "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-2 (induced by source paper "finite dimensional statistical inference"):
  with members:
    - "convolution operations arising from vandermonde matrices"
    - "on the optimal stacking of noisy observations"
    - "free deconvolution for signal processing applications"

Connected through hyperedge HE-3 (induced by source paper "on the optimal stacking of noisy observations"):
  with members:
    - "finite dimensional statistical inference"

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

Connected through hyperedge HE-5 (induced by source paper "asymptotic behaviour of random vandermonde matrices with entries on the unit circle"):
  with members:
    - "cooperative spectrum sensing using random matrix theory"
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"
    - "reconstruction of multidimensional signals from irregular noisy samples"

Connected through hyperedge HE-6 (induced by source paper "random vandermonde matrices part i fundamental results"):
  with members:
    - "random vandermonde matrices part ii applications"
    - "free deconvolution for signal processing applications"

Connected through hyperedge HE-7 (induced by source paper "cognitive ofdm network sensing a free probability approach"):
  with members:
    - "free deconvolution for signal processing applications"

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)