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: "eigen inference for energy estimation of multiple sources"
  Abstract: In this paper, a new method is introduced to blindly estimate the transmit power of multiple signal sources in multiantenna fading channels, when the number of sensing devices and the number of available samples are sufficiently large compared to the number of sources. Recent advances in the field of large dimensional random matrix theory are used that result in a simple and computationally efficient consistent estimator of the power of each source. A criterion to determine the minimum number of sensors and the minimum number of samples required to achieve source separation is then introduced. Simulations are performed that corroborate the theoretical claims and show that the proposed power estimator largely outperforms alternative power inference techniques.

Connected through hyperedge HE-1 (induced by source paper "large system analysis of a glrt for detection with large sensor arrays in temporally white noise"):
  with members:
    - "improved subspace estimation for multivariate observations of high dimension the deterministic signals case"

Connected through hyperedge HE-2 (induced by source paper "robust estimates of covariance matrices in the large dimensional regime"):
  with members:
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "a deterministic equivalent for the analysis of correlated mimo multiple access channels"

Connected through hyperedge HE-3 (induced by source paper "asymptotic analysis of a glrt test for detection with large sensor arrays"):
  with members:
    - "improved subspace estimation for multivariate observations of high dimension the deterministic signals case"

Connected through hyperedge HE-4 (induced by source paper "statistical inference in large antenna arrays under unknown noise pattern"):
  with members:
    - "cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices"
    - "cooperative spectrum sensing using random matrix theory"
    - "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals"

Connected through hyperedge HE-5 (induced by source paper "eigenvalue estimation of parameterized covariance matrices of large dimensional data"):
  with members:
    - "improved subspace estimation for multivariate observations of high dimension the deterministic signals case"

Connected through hyperedge HE-6 (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"
    - "finite dimensional statistical inference"
    - "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-7 (induced by source paper "convolution operations arising from vandermonde matrices"):
  with members:
    - "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"
    - "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)