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: "multi processor approximate message passing using lossy compression"
  Abstract: In this paper, a communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed. We perform lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quality. In the proposed framework, a new state evolution formulation takes the quantization error into account, and analytically determines the coding rate required in each iteration. Two approaches for allocating the coding rate, an online back-tracking heuristic and an optimal allocation scheme based on dynamic programming, provide significant reductions in communication costs.

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"
    - "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"
    - "two part reconstruction with noisy sudocodes"

Connected through hyperedge HE-2 (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-3 (induced by source paper "source coding optimization for distributed average consensus"):
  with members:
    - "performance limits for noisy multimeasurement vector problems"
    - "an overview of multi processor approximate message passing"
    - "statistical physics and information theory perspectives on linear inverse problems"
    - "optimal trade offs in multi processor approximate message passing"
    - "distributed parameter estimation with quantized communication via running average"
    - "on constrained randomized quantization"
    - "toward resource optimal consensus over the wireless medium"
    - "non asymptotic analysis of an optimal algorithm for network constrained averaging with noisy links"

Connected through hyperedge HE-4 (induced by source paper "multiprocessor approximate message passing with column wise partitioning"):
  with members:
    - "optimal trade offs in multi processor approximate message passing"
    - "performance trade offs in multi processor approximate message passing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-5 (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"
    - "mismatched estimation in large linear systems"
    - "signal estimation with additive error metrics in compressed sensing"
    - "distributed compressed sensing for static and time varying networks"
    - "performance regions in compressed sensing from noisy measurements"

Connected through hyperedge HE-6 (induced by source paper "performance trade offs in multi processor approximate message passing"):
  with members:
    - "optimal trade offs in multi processor approximate message passing"
    - "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"
    - "distributed sparse signal recovery for sensor networks"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "on sparse vector recovery performance in structurally orthogonal matrices via lasso"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "analysis of compressed sensing with spatially coupled orthogonal matrices"

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.DC(Distributed, Parallel, and Cluster Computing)