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: "decentralized sparsity regularized rank minimization algorithms and applications"
  Abstract: Given a limited number of entries from the superposition of a low-rank matrix plus the product of a known compression matrix times a sparse matrix, recovery of the low-rank and sparse components is a fundamental task subsuming compressed sensing, matrix completion, and principal components pursuit. This paper develops algorithms for decentralized sparsity-regularized rank minimization over networks, when the nuclear- and l1-norm are used as surrogates to the rank and nonzero entry counts of the sought matrices, respectively. While nuclear-norm minimization has well-documented merits when centralized processing is viable, non-separability of the singular-value sum challenges its decentralized minimization. To overcome this limitation, leveraging an alternative characterization of the nuclear norm yields a separable, yet non-convex cost minimized via the alternating-direction method of multipliers. Interestingly, if the decentralized (non-convex) estimator converges, under certain conditions it provably attains the global optimum of its centralized counterpart. As a result, this paper bridges the performance gap between centralized and in-network decentralized, sparsity-regularized rank minimization. This, in turn, facilitates (stable) recovery of the low rank and sparse model matrices through reduced-complexity per-node computations, and affordable message passing among single-hop neighbors. Several application domains are outlined to highlight the generality and impact of the proposed framework. These include unveiling traffic anomalies in backbone networks, and predicting networkwide path latencies. Simulations with synthetic and real network data confirm the convergence of the novel decentralized algorithm, and its centralized performance guarantees.

Connected through hyperedge HE-1 (induced by source paper "signal recovery on graphs variation minimization"):
  with members:
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs frequency analysis"
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"
    - "compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"

Connected through hyperedge HE-2 (induced by source paper "per block convex data modeling by accelerated stochastic approximation"):
  with members:
    - "sparsity cognizant total least squares for perturbed compressive sampling"

Connected through hyperedge HE-3 (induced by source paper "estimating traffic and anomaly maps via network tomography"):
  with members:
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "principal component pursuit with reduced linear measurements"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"):
  with members:
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "compressive principal component pursuit"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "dynamic anomalography tracking network anomalies via sparsity and low rank"):
  with members:
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "fast distributed gradient methods"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "stable principal component pursuit"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "rank regularization and bayesian inference for tensor completion and extrapolation"):
  with members:
    - "online identification and tracking of subspaces from highly incomplete information"
    - "matrix completion with noise"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

Connected through hyperedge HE-7 (induced by source paper "learning efficient sparse and low rank models"):
  with members:
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "learning efficient structured sparse models"
    - "robust sparse analysis regularization"
    - "robust pca via outlier pursuit"
    - "fast inference in sparse coding algorithms with applications to object recognition"
    - "collaborative sources identification in mixed signals via hierarchical sparse modeling"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "robust principal component analysis"

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.MA(Multiagent Systems)