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: "multi view in lensless compressive imaging"
  Abstract: Multi-view images are acquired by a lensless compressive imaging architecture, which consists of an aperture assembly and multiple sensors. The aperture assembly consists of a two dimensional array of aperture elements whose transmittance can be individually controlled to implement a compressive sensing matrix. For each transmittance pattern of the aperture assembly, each of the sensors takes a measurement. The measurement vectors from the multiple sensors represent multi-view images of the same scene. We present theoretical framework for multi-view reconstruction and experimental results for enhancing quality of image using multi-view.

Connected through hyperedge HE-1 (induced by source paper "lensless photography with only an image sensor"):
  with members:
    - "reconstruction free action inference from compressive imagers"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing and parallel acquisition"):
  with members:
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "self calibration and biconvex compressive sensing"
    - "refined analysis of sparse mimo radar"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "wireless compressive sensing for energy harvesting sensor nodes"
    - "an algorithm for variable density sampling with block constrained acquisition"

Connected through hyperedge HE-3 (induced by source paper "uniform recovery from subgaussian multi sensor measurements"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "local sparsity and recovery of fusion frames structured signals"
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "optimal sparse recovery for multi sensor measurements"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "the restricted isometry property for random block diagonal matrices"

Connected through hyperedge HE-4 (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"
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "minimum complexity pursuit for universal compressed sensing"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "generalized alternating projection based total variation minimization for compressive sensing"):
  with members:
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"
    - "convergence of the generalized alternating projection algorithm for compressive sensing"
    - "tree structure bayesian compressive sensing for video"
    - "lensless imaging by compressive sensing"
    - "coded aperture compressive temporal imaging"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "lensless compressive imaging"):
  with members:
    - "compressive hyperspectral imaging with side information"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "bayesian nonparametric dictionary learning for compressed sensing mri"
    - "from denoising to compressed sensing"
    - "a new compressive video sensing framework for mobile broadcast"
    - "lensless imaging by compressive sensing"
    - "surveillance video processing using compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "convergence of the generalized alternating projection algorithm for compressive sensing"):
  with members:
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"
    - "tree structure bayesian compressive sensing for video"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "signal to noise ratio in lensless compressive imaging"):
  with members:
    - "a new compressive video sensing framework for mobile broadcast"
    - "lensless imaging by compressive sensing"
    - "lensless compressive sensing imaging"
    - "surveillance video processing using 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.IT(Information Theory)