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 "random vandermonde matrices part ii applications"):
  Abstract: In this paper, we review some potential applications of random Vandermonde matrices in the field of signal process ing and wireless communications. Using asymptotic results based on the theory of random Vandermonde matrices, we show through several application examples, namely deconvolution, wireless capacity analysis and sampling theory, the research potential of this theory. Quite surprisingly, in nearly all the cases, the asymptotic results turn out to be valid for dimensions which are of interest for the community. The simulations confirm th at random matrix theory/free probability theory are once more a unique tool to better understand the behavior of the eigenvalues of matrices.

Includes members:
  Member "cooperative spectrum sensing using random matrix theory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligent wireless communications enabled by cognitive radio and machine learning")
  Member "channel capacity estimation using free probability theory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolution operations arising from vandermonde matrices")
  Member "performance of linear field reconstruction techniques with noise and uncertain sensor locations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on bandlimited field estimation from samples recorded by a location unaware mobile sensor")
  Member "random vandermonde matrices part i fundamental results":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the impact of physical channel on performance of subspace based channel estimation in massive mimo systems")
  Member "free deconvolution for signal processing applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolution operations arising from vandermonde matrices")
  Member "mutual information and minimum mean square error in gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")

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)