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 "statistical inference in large antenna arrays under unknown noise pattern"):
  Abstract: In this paper, a general information-plus-noise transmission model is assumed, the receiver end of which is composed of a large number of sensors and is unaware of the noise correlation pattern. For this model, under an isotropy assumption between signal and noise left- and right-eigenspaces, a set of results is provided for the receiver to perform statistical eigen-inference on the information part. In particular, we introduce new methods for the detection, counting, and the power and subspace estimation of multiple sources composing the information part of the transmission. The theoretical performance of some of these techniques is also discussed. An exemplary application of these methods to array processing with unknown time correlated noise is then studied in greater detail, leading to a novel MUSIC-like algorithm.

Includes members:
  Member "eigen inference for energy estimation of multiple sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large system analysis of a glrt for detection with large sensor arrays in temporally white noise")
  Member "cooperative spectrum sensing based on the limiting eigenvalue ratio distribution in wishart matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decentralized eigenvalue algorithms for distributed signal detection in cognitive networks")
  Member "cooperative spectrum sensing using random matrix theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligent wireless communications enabled by cognitive radio and machine learning")
  Member "a central limit theorem for the sinr at the lmmse estimator output for large dimensional signals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation")

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)