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: "distributed compressed sensing for static and time varying networks"
  Abstract: We consider the problem of in-network compressed sensing from distributed measurements. Every agent has a set of measurements of a signal $x$, and the objective is for the agents to recover $x$ from their collective measurements using only communication with neighbors in the network. Our distributed approach to this problem is based on the centralized Iterative Hard Thresholding algorithm (IHT). We first present a distributed IHT algorithm for static networks that leverages standard tools from distributed computing to execute in-network computations with minimized bandwidth consumption. Next, we address distributed signal recovery in networks with time-varying topologies. The network dynamics necessarily introduce inaccuracies to our in-network computations. To accommodate these inaccuracies, we show how centralized IHT can be extended to include inexact computations while still providing the same recovery guarantees as the original IHT algorithm. We then leverage these new theoretical results to develop a distributed version of IHT for time-varying networks. Evaluations show that our distributed algorithms for both static and time-varying networks outperform previously proposed solutions in time and bandwidth by several orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "spatial compressive sensing for mimo radar"

Connected through hyperedge HE-2 (induced by source paper "distributed quantization for sparse time sequences"):
  with members:
    - "serial quantization for representing sparse signals"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "compressive sensing over networks"
    - "distributed compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "deterministic and randomized diffusion based iterative generalized hard thresholding difight for distributed sparse signal recovery"):
  with members:
    - "next in network nonconvex optimization"
    - "greedy sparsity promoting algorithms for distributed learning"
    - "on the learning behavior of adaptive networks part ii performance analysis"
    - "sparse distributed learning based on diffusion adaptation"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "diffusion adaptation over networks"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"

Connected through hyperedge HE-4 (induced by source paper "compressed anomaly detection with multiple mixed observations"):
  with members:
    - "compressed hypothesis testing to mix or not to mix"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "on the limits of sequential testing in high dimensions"
    - "statistical compressed sensing of gaussian mixture models"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "iterative hard thresholding for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-5 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"

Connected through hyperedge HE-6 (induced by source paper "an overview of multi processor approximate message passing"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "multiprocessor approximate message passing with column wise partitioning"
    - "hybrid approximate message passing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "optimal trade offs in multi processor approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "performance trade offs in multi processor approximate message passing"

Connected through hyperedge HE-7 (induced by source paper "optimal trade offs in multi processor approximate message passing"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "hybrid approximate message passing"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "mismatched estimation in large linear systems"
    - "signal estimation with additive error metrics in compressed sensing"
    - "performance regions in compressed sensing from noisy measurements"

Connected through hyperedge HE-8 (induced by source paper "next in network nonconvex optimization"):
  with members:
    - "multi agent distributed optimization via inexact consensus admm"
    - "hybrid random deterministic parallel algorithms for nonconvex big data optimization"
    - "parallel selective algorithms for big data optimization"
    - "distributed detection and estimation in wireless sensor networks"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "diffusion adaptation strategies for distributed estimation over gaussian markov random fields"
    - "distributed maximum likelihood sensor network localization"
    - "sparse distributed learning based on diffusion adaptation"

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)