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: "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.

Connected through hyperedge HE-1 (induced by source paper "side information in robust principal component analysis algorithms and applications"):
  with members:
    - "fast robust pca on graphs"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "robust principal component analysis on graphs"
    - "justification of logarithmic loss via the benefit of side information"
    - "deception with side information in biometric authentication systems"
    - "compressed sensing with side information geometrical interpretation and performance bounds"
    - "robust recovery of subspace structures by low rank representation"
    - "stable principal component pursuit"

Connected through hyperedge HE-2 (induced by source paper "compressive online robust principal component analysis with optical flow for video foreground background separation"):
  with members:
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "robust pca with partial subspace knowledge"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "compressive principal component pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "performance bounds for sparse signal reconstruction with multiple side information"):
  with members:
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressed sensing with side information geometrical interpretation and performance bounds"
    - "robust pca with partial subspace knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

Connected through hyperedge HE-4 (induced by source paper "on the role and the importance of features for background modeling and foreground detection"):
  with members:
    - "robust 3d action recognition through sampling local appearances and global distributions"
    - "estimating sparse signals with smooth support via convex programming and block sparsity"
    - "deep learning in neural networks"
    - "variational inference for background subtraction in infrared imagery"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "background subtraction separating the modeling and the inference"
    - "complex background subtraction by pursuing dynamic spatio temporal models"
    - "adaptive low rank and sparse decomposition of video using compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"):
  with members:
    - "compressed sensing with side information geometrical interpretation and performance bounds"

Connected through hyperedge HE-6 (induced by source paper "compressive sensing approaches for autonomous object detection in video sequences"):
  with members:
    - "sparse modeling for image and vision processing"
    - "optimization with sparsity inducing penalties"

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)