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: "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
  Abstract: This work considers reconstructing a target signal in a context of distributed sparse sources. We propose an efficient reconstruction algorithm with the aid of other given sources as multiple side information (SI). The proposed algorithm takes advantage of compressive sensing (CS) with SI and adaptive weights by solving a proposed weighted $n$-$\ell_{1}$ minimization. The proposed algorithm computes the adaptive weights in two levels, first each individual intra-SI and then inter-SI weights are iteratively updated at every reconstructed iteration. This two-level optimization leads the proposed reconstruction algorithm with multiple SI using adaptive weights (RAMSIA) to robustly exploit the multiple SIs with different qualities. We experimentally perform our algorithm on generated sparse signals and also correlated feature histograms as multiview sparse sources from a multiview image database. The results show that RAMSIA significantly outperforms both classical CS and CS with single SI, and RAMSIA with higher number of SIs gained more than the one with smaller number of SIs.

Connected through hyperedge HE-1 (induced by source paper "online decomposition of compressive streaming data using n ell_1 cluster weighted minimization"):
  with members:
    - "approximation algorithms for model based compressive sensing"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressive principal component pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

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"
    - "adaptive rate compressive sensing using side information"
    - "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 "incorporating prior information in compressive online robust principal component analysis"):
  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"
    - "living on the edge phase transitions in convex programs with random data"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "compressive principal component pursuit"
    - "support predicted modified cs for recursive robust principal components pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-4 (induced by source paper "distributed coding of multiview sparse sources with joint recovery"):
  with members:
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "compressed sensing with side information geometrical interpretation and performance bounds"

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)