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 sparse signal recovery for sensor networks"
  Abstract: We propose a distributed algorithm for sparse signal recovery in sensor networks based on Iterative Hard Thresholding (IHT). 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 at a minimal communication cost and with low computational complexity. A naive distributed implementation of IHT would require global communication of every agent's full state in each iteration. We find that we can dramatically reduce this communication cost by leveraging solutions to the distributed top-K problem in the database literature. Evaluations show that our algorithm requires up to three orders of magnitude less total bandwidth than the best-known distributed basis pursuit method.

Connected through hyperedge HE-1 (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-2 (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"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-3 (induced by source paper "performance trade offs in multi processor approximate message passing"):
  with members:
    - "optimal trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "mismatched estimation in large linear systems"
    - "two part reconstruction with noisy sudocodes"
    - "distributed compressed sensing for static and time varying networks"
    - "performance regions in compressed sensing from noisy measurements"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "multi processor approximate message passing using lossy compression"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "non negative principal component analysis message passing algorithms and sharp asymptotics"
    - "bilinear generalized approximate message passing part i derivation"
    - "distributed compressed sensing for static and time varying networks"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "greedy sparsity promoting algorithms for distributed learning"):
  with members:
    - "sparsity aware learning and compressed sensing an overview"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "mapreduce is good enough if all you have is a hammer throw away everything that s not a nail"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "omp based joint sparsity pattern recovery under communication constraints"):
  with members:
    - "distributed greedy pursuit algorithms"
    - "subspace recovery from structured union of subspaces"
    - "applications of compressed sensing in communications networks"
    - "theory and applications of compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "distributed compressed sensing for static and time varying networks"):
  with members:
    - "distributed soft thresholding for sparse signal recovery"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "a fast distributed proximal gradient method"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "convergence rates of inexact proximal gradient methods for convex optimization"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "distributed approximate message passing for compressed sensing"):
  with members:
    - "structured compressed sensing from theory to applications"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "optimal aggregation algorithms for middleware"

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)