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 "adaptive rate compressive sensing using side information"):
  Abstract: We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information. Our first method utilizes extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprise the images in the video sequence. Instead, we use the side information to predict the number of significant coefficients in the signal at the next time instant. For each image in the video sequence, our techniques specify a fixed number of spatially-multiplexed CS measurements to acquire, and adjust this quantity from image to image. Our strategies are developed in the specific context of background subtraction for surveillance video, and we experimentally validate the proposed methods on real video sequences.

Includes members:
  Member "task driven adaptive statistical compressive sensing of gaussian mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information")
  Member "the stone transform multi resolution image enhancement and real time compressive video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi resolution compressive sensing reconstruction")
  Member "dynamic anomalography tracking network anomalies via sparsity and low rank":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "static and dynamic robust pca and matrix completion a review")
  Member "ls cs residual ls cs compressive sensing on least squares residual":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "sequential compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations")
  Member "modified cs modifying compressive sensing for problems with partially known support":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "kalman filtered compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "designing recurrent neural networks by unfolding an l1 l1 minimization algorithm")

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.CV(Computer Vision and Pattern Recognition)