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 "compressed sensing and parallel acquisition"):
  Abstract: Parallel acquisition systems arise in various applications in order to moderate problems caused by insufficient measurements in single-sensor systems. These systems allow simultaneous data acquisition in multiple sensors, thus alleviating such problems by providing more overall measurements. In this work we consider the combination of compressed sensing with parallel acquisition. We establish the theoretical improvements of such systems by providing recovery guarantees for which, subject to appropriate conditions, the number of measurements required per sensor decreases linearly with the total number of sensors. Throughout, we consider two different sampling scenarios -- distinct (corresponding to independent sampling in each sensor) and identical (corresponding to dependent sampling between sensors) -- and a general mathematical framework that allows for a wide range of sensing matrices (e.g., subgaussian random matrices, subsampled isometries, random convolutions and random Toeplitz matrices). We also consider not just the standard sparse signal model, but also the so-called sparse in levels signal model. This model includes both sparse and distributed signals and clustered sparse signals. As our results show, optimal recovery guarantees for both distinct and identical sampling are possible under much broader conditions on the so-called sensor profile matrices (which characterize environmental conditions between a source and the sensors) for the sparse in levels model than for the sparse model. To verify our recovery guarantees we provide numerical results showing phase transitions for a number of different multi-sensor environments.

Includes members:
  Member "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class")
  Member "optimal sparse recovery for multi sensor measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class")
  Member "wireless compressive sensing over fading channels with distributed sparse random projections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on analog gradient descent learning over multiple access fading channels")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "refined analysis of sparse mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "breaking the coherence barrier a new theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "multi view in lensless compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lensless photography with only an image sensor")
  Member "wireless compressive sensing for energy harvesting sensor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "accelerating federated learning via momentum gradient descent")

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)