human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems"):
  Abstract: Real-world applications such as magnetic resonance imaging with multiple coils, multiuser communication, and diffuse optical tomography often assume a linear model, where several sparse signals sharing common sparse supports are acquired by several measurement matrices and then contaminated by noise. Multimeasurement vector (MMV) problems consider the estimation or reconstruction of such signals. In different applications, the estimation error that we want to minimize could be the mean squared error or other metrics, such as the mean absolute error and the support set error. Seeing that minimizing different error metrics is useful in MMV problems, we study information-theoretic performance limits for MMV signal estimation with arbitrary additive error metrics. We also propose a message passing algorithmic framework that achieves the optimal performance, and rigorously prove the optimality of our algorithm for a special case. We further conjecture the optimality of our algorithm for some general cases and back it up through numerical examples. As an application of our MMV algorithm, we propose a novel setup for active user detection in multiuser communication and demonstrate the promise of our proposed setup.

Includes members:
  Member "approximate message passing decoder and capacity achieving sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "user activity detection in massive random access compressed sensing vs coded slotted aloha":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo")
  Member "performance analysis of approximate message passing for distributed compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "theoretical bounds on map estimation in distributed sensing networks")
  Member "statistical physics and information theory perspectives on linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an overview of multi processor approximate message passing")
  Member "sparse multinomial logistic regression via approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hybrid approximate message passing")
  Member "signal estimation with additive error metrics in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "efficient high dimensional inference in the multiple measurement vector problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "performance regions in compressed sensing from noisy measurements":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")

Question: Please tell me which class the source paper 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)