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: "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"
  Abstract: This paper presents a novel projection-based adaptive algorithm for sparse signal and system identification. The sequentially observed data are used to generate an equivalent sequence of closed convex sets, namely hyperslabs. Each hyperslab is the geometric equivalent of a cost criterion, that quantifies “data mismatch.” Sparsity is imposed by the introduction of appropriately designed weighted l1 balls and the related projection operator is also derived. The algorithm develops around projections onto the sequence of the generated hyperslabs as well as the weighted l1 balls. The resulting scheme exhibits linear dependence, with respect to the unknown system's order, on the number of multiplications/additions and an O(Llog2L) dependence on sorting operations, where L is the length of the system/signal to be estimated. Numerical results are also given to validate the performance of the proposed method against the Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm and two very recently developed adaptive sparse schemes that fuse arguments from the LMS/RLS adaptation mechanisms with those imposed by the lasso rational.

Connected through hyperedge HE-1 (induced by source paper "robust and computationally efficient anomaly detection using powers of two networks"):
  with members:
    - "efficient gan based anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "a survey of model compression and acceleration for deep neural networks"
    - "context encoders feature learning by inpainting"
    - "plug and play cnn for crowd motion analysis an application in abnormal event detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "conditional deep learning for energy efficient and enhanced pattern recognition"

Connected through hyperedge HE-2 (induced by source paper "dynamic mode decomposition for compressive system identification"):
  with members:
    - "using dynamic mode decomposition for real time background foreground separation in video"
    - "identification of sparse linear operators"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-3 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "adaptive least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"

Connected through hyperedge HE-5 (induced by source paper "a survey of stochastic simulation and optimization methods in signal processing"):
  with members:
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "communication efficient distributed dual coordinate ascent"
    - "on the convergence of approximate message passing with arbitrary matrices"

Connected through hyperedge HE-6 (induced by source paper "least mean squares estimation of graph signals"):
  with members:
    - "sampling of graph signals with successive local aggregations"
    - "toward an uncertainty principle for weighted graphs"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "on the degrees of freedom of signals on graphs"
    - "signal recovery on graphs fundamental limits of sampling strategies"
    - "a distributed tracking algorithm for reconstruction of graph signals"
    - "signal recovery on graphs variation minimization"

Connected through hyperedge HE-7 (induced by source paper "proximal multitask learning over networks with sparsity inducing coregularization"):
  with members:
    - "unsupervised diffusion based lms for node specific parameter estimation over wireless sensor networks"
    - "diffusion lms over multitask networks"
    - "distributed diffusion based lms for node specific adaptive parameter estimation"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "multitask diffusion adaptation over asynchronous networks"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "diffusion lms for clustered multitask networks"
    - "multitask diffusion adaptation over networks"

Connected through hyperedge HE-8 (induced by source paper "sparsity aware learning and compressed sensing an overview"):
  with members:
    - "experiments with random projection"
    - "compressive principal component pursuit"
    - "stable image reconstruction using total variation minimization"
    - "recovering low rank matrices from few coefficients in any basis"
    - "structured compressed sensing from theory to applications"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "concentration based guarantees for low rank matrix reconstruction"

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)