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 "learning dynamic compressive sensing models"):
  Abstract: Random sampling in compressive sensing (CS) enables the compression of large amounts of input signals in an efficient manner, which is useful for many applications. CS reconstructs the compressed signals exactly with overwhelming probability when incoming data can be sparsely represented with a fixed number of components, which is one of the drawbacks of CS frameworks because the signal sparsity in many dynamic systems changes over time. We present a new CS framework that handles signals without the fixed sparsity assumption by incorporating the distribution of signal sparsity. We show that the signal recovery success in our beta distribution modeling is more accurate than the success probability analysis in the CS framework. Alternatively, the success or failure of signal recovery can be relaxed, and the numbers of components included in signal recoveries can be represented with a probability distribution. We show this distribution is skewed to the right and naturally represented by the gamma distribution. Experimental results confirm the accuracy of our modeling in the context of dynamic signal sparsity.

Includes members:
  Member "dynamic compressive sensing of time varying signals via approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an approximate message passing framework for side information")
  Member "estimating unknown sparsity in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "sequential compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations")
  Member "multi label prediction via compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crowd scene analysis by output encoding")

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)