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: "field reconstruction in sensor networks with coverage holes and packet losses"
  Abstract: Environmental monitoring is often performed through wireless sensor networks, by randomly deploying sensor nodes over the geographical region of interest. Sensors sample a physical phenomenon (the so-called field) and send their measurements to a sink, which is in charge of reconstructing the field from such irregular samples. In this work, we focus on scenarios of practical interest where the sensor deployment is unfeasible in certain areas of the geographical region (e.g., due to terrain asperities), and the delivery of sensor measurements to the sink may fail (e.g., due to fading or to transmission collisions among sensors simultaneously accessing the wireless medium). Under these conditions, we carry out an asymptotic analysis and evaluate the quality of the estimation of a field defined over a d -dimensional domain (d ≥ 1) when the sink uses linear filtering as a reconstruction technique. Specifically, given the matrix V representing the sampling system, we let the size of V go to infinity and its aspect ratio have a finite limit bounded away from zero. Then, we provide both the moments and density of the limiting spectral distribution of VVH, in terms of those obtained when the samples collected by the sink correspond to locations that are uniformly distributed over the geographical area. By using such asymptotic results, we approximate the mean square error on the estimated field through the η-transform of VVH and we derive the sensor network performance under the conditions described above.

Connected through hyperedge HE-1 (induced by source paper "convolution operations arising from vandermonde matrices"):
  with members:
    - "eigen inference for energy estimation of multiple sources"
    - "finite dimensional statistical inference"
    - "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.NI(Networking and Internet Architecture)