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 trade offs in multi processor approximate message passing"):
  Abstract: We consider large-scale linear inverse problems in Bayesian settings. Our general approach follows a recent line of work that applies the approximate message passing (AMP) framework in multi-processor (MP) computational systems by storing and processing a subset of rows of the measurement matrix along with corresponding measurements at each MP node. In each MP-AMP iteration, nodes of the MP system and its fusion center exchange lossily compressed messages pertaining to their estimates of the input. There is a trade-off between the physical costs of the reconstruction process including computation time, communication loads, and the reconstruction quality, and it is impossible to simultaneously minimize all the costs. We pose this minimization as a multi-objective optimization problem (MOP), and study the properties of the best trade-offs (Pareto optimality) in this MOP. We prove that the achievable region of this MOP is convex, and conjecture how the combined cost of computation and communication scales with the desired mean squared error. These properties are verified numerically.

Includes members:
  Member "optimal trade offs in multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information")
  Member "multi processor approximate message passing using lossy compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "mismatched estimation in large linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "two part reconstruction with noisy sudocodes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "distributed compressed sensing for static and time varying networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "performance regions in compressed sensing from noisy measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "distributed sparse signal recovery for sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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)