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: "reconstruction of multidimensional signals from irregular noisy samples"
  Abstract: We focus on a multidimensional field with uncorrelated spectrum and study the quality of the reconstructed signal when the field samples are irregularly spaced and affected by independent and identically distributed noise. More specifically, we apply linear reconstruction techniques and take the mean-square error (MSE) of the field estimate as a metric to evaluate the signal reconstruction quality. We find that the MSE analysis could be carried out by using the closed-form expression of the eigenvalue distribution of the matrix representing the sampling system. Unfortunately, such distribution is still unknown. Thus, we first derive a closed-form expression of the distribution moments, and we find that the eigenvalue distribution tends to the Marcenko-Pastur distribution as the field dimension goes to infinity. Finally, by using our approach, we derive a tight approximation to the MSE of the reconstructed field.

Connected through hyperedge HE-1 (induced by source paper "spatial degrees of freedom of mimo systems in line of sight environment"):
  with members:
    - "capacity scaling of wireless ad hoc networks shannon meets maxwell"
    - "asymptotic behaviour of random vandermonde matrices with entries on the unit circle"

Connected through hyperedge HE-2 (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"
    - "channel capacity estimation using free probability theory"
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"
    - "vandermonde frequency division multiplexing for cognitive radio"
    - "free deconvolution for signal processing applications"

Connected through hyperedge HE-3 (induced by source paper "field reconstruction in sensor networks with coverage holes and packet losses"):
  with members:
    - "convolution operations arising from vandermonde matrices"
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"

Connected through hyperedge HE-4 (induced by source paper "on the d dimensional quasi equally spaced sampling"):
  with members:
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"
    - "random vandermonde matrices part ii applications"
    - "random vandermonde matrices part i fundamental results"
    - "free deconvolution for signal processing applications"

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"
    - "channel capacity estimation using free probability theory"
    - "performance of linear field reconstruction techniques with noise and uncertain sensor locations"

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)