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 "binary compressive sensing via analog fountain coding"):
  Abstract: In this paper, a compressive sensing (CS) approach is proposed for sparse binary signals' compression and reconstruction based on analog fountain codes (AFCs). In the proposed scheme, referred to as the analog fountain compressive sensing (AFCS), each measurement is generated from a linear combination of    $L$   randomly selected binary signal elements with real weight coefficients. The weight coefficients are chosen from a finite weight set and   $L$  , called measurement degree, is obtained based on a predefined degree distribution function. We propose a simple verification based reconstruction algorithm for the AFCS in the noiseless case. The proposed verification based decoder is analyzed through SUM-OR tree analytical approach and an optimization problem is formulated to find the optimum measurement degree to minimize the number of measurements required for the reconstruction of binary sparse signals. We show that in the AFCS, the number of required measurements is of   ${\cal O}(-n\log(1-k/n))$   , where   $n$   is the signal length and   $L=k$   is the signal sparsity level. Simulation results show that the AFCS can perfectly recover all non-zero elements of the sparse binary signal with a significantly reduced number of measurements, compared to the conventional binary CS and   $\ell_1$  -minimization approaches in a wide range of signal to noise ratios (SNRs) by using the standard message passing decoder. Finally, we show a practical application of the AFCS for the sparse event detection in wireless sensor networks (WSNs), where the sensors' readings can be treated as measurements from the CS point of view.

Includes members:
  Member "near capacity adaptive analog fountain codes for wireless channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two layer coded channel access with collision resolution design and analysis")
  Member "wireless compressive sensing for energy harvesting sensor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accelerating federated learning via momentum gradient descent")
  Member "measure what should be measured progress and challenges in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "message passing algorithms for compressed sensing i motivation and construction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "random access for massive machine type communications")
  Member "weighted superimposed codes and constrained integer compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "restricted isometry property of random subdictionaries")
  Member "bayesian compressive sensing via belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")

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)