human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "signal processing in large systems a new paradigm"):
  Abstract: For a long time, detection and parameter estimation methods for signal processing have relied on asymptotic statistics as the number $n$ of observations of a population grows large comparatively to the population size $N$, i.e. $n/N\to \infty$. Modern technological and societal advances now demand the study of sometimes extremely large populations and simultaneously require fast signal processing due to accelerated system dynamics. This results in not-so-large practical ratios $n/N$, sometimes even smaller than one. A disruptive change in classical signal processing methods has therefore been initiated in the past ten years, mostly spurred by the field of large dimensional random matrix theory. The early works in random matrix theory for signal processing applications are however scarce and highly technical. This tutorial provides an accessible methodological introduction to the modern tools of random matrix theory and to the signal processing methods derived from them, with an emphasis on simple illustrative examples.

Includes members:
  Member "random beamforming over quasi static and fading channels a deterministic equivalent approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation")
  Member "improved subspace estimation for multivariate observations of high dimension the deterministic signals case":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "report on two step knowledge aided iterative esprit algorithm")
  Member "eigen inference for energy estimation of multiple sources":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large system analysis of a glrt for detection with large sensor arrays in temporally white noise")
  Member "finite dimensional statistical inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolution operations arising from vandermonde matrices")
  Member "random beamforming over correlated fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotic analysis of double scattering channels")
  Member "a deterministic equivalent for the analysis of correlated mimo multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation")
  Member "on the capacity achieving covariance matrix for frequency selective mimo channels using the asymptotic approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "cooperative spectrum sensing using random matrix theory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligent wireless communications enabled by cognitive radio and machine learning")

Question: Please tell me which class the source paper 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)