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: "the stone transform multi resolution image enhancement and real time compressive video"
  Abstract: Compressed sensing enables the reconstruction of high-resolution signals from under-sampled data. While compressive methods simplify data acquisition, they require the solution of difficult recovery problems to make use of the resulting measurements. This article presents a new sensing framework that combines the advantages of both conventional and compressive sensing. Using the proposed \stone transform, measurements can be reconstructed instantly at Nyquist rates at any power-of-two resolution. The same data can then be "enhanced" to higher resolutions using compressive methods that leverage sparsity to "beat" the Nyquist limit. The availability of a fast direct reconstruction enables compressive measurements to be processed on small embedded devices. We demonstrate this by constructing a real-time compressive video camera.

Connected through hyperedge HE-1 (induced by source paper "multi resolution compressive sensing reconstruction"):
  with members:
    - "multi resolution compressed sensing via approximate message passing"
    - "from denoising to compressed sensing"
    - "lensless imaging by compressive sensing"
    - "multi view in lensless compressive imaging"
    - "minimum complexity pursuit for universal compressed sensing"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "fast integral image estimation at 1 measurement rate"):
  with members:
    - "reconstruction free action inference from compressive imagers"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "correlation estimation from compressed images"

Connected through hyperedge HE-3 (induced by source paper "multi resolution compressed sensing via approximate message passing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "statistical physics and approximate message passing algorithms for sparse linear estimation problems in signal processing and coding theory"
    - "from denoising to compressed sensing"
    - "spatially scalable compressed image sensing with hybrid transform and inter layer prediction model"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "video compressive sensing for spatial multiplexing cameras using motion flow models"):
  with members:
    - "lisens a scalable architecture for video compressive sensing"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "compressive coded aperture keyed exposure imaging with optical flow reconstruction"
    - "coded aperture compressive temporal imaging"
    - "compressive acquisition of dynamic scenes"

Connected through hyperedge HE-5 (induced by source paper "adaptive rate compressive sensing using side information"):
  with members:
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "sequential compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "breaking the coherence barrier a new theory for compressed sensing"):
  with members:
    - "a partial derandomization of phaselift using spherical designs"
    - "structure dependent sampling in compressed sensing theoretical guarantees for tight frames"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "on asymptotic incoherence and its implications for compressed sensing of inverse problems"
    - "lensless imaging by compressive sensing"
    - "an analysis of block sampling strategies in compressed sensing"
    - "communications inspired projection design with application to compressive sensing"

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.CV(Computer Vision and Pattern Recognition)