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 "compressive link acquisition in multiuser communications"):
  Abstract: An important sensing operation is to detect the presence of specific signals with unknown transmission parameters. This task, referred to as “link acquisition,” is typically a sequential search over the transmitted signal space. Recently, the use of sparsity in similar estimation or detection problems has received considerable attention. These works typically focus on the benefits of compressed sensing, but not generally on the cost brought by sparse recovery. Our goal is to examine the tradeoff in complexity and performance when using sparse recovery with compressed or uncompressed samples. To do so, we propose a compressive sparsity aware (CSA) acquisition scheme, where a compressive multichannel sampling (CMS) front-end is followed by a sparsity regularized likelihood ratio test (SR-LRT) module. The CSA scheme borrows insights from the models studied in sub-Nyquist sampling and finite rate of innovation (FRI) signals. We further optimize the CMS front-end by maximizing the average Kullback-Leibler distance of all the hypotheses in the SR-LRT. We compare the CSA scheme vis-a-vis other popular alternatives in terms of performance and complexity. Simulations suggest that one can use the CSA scheme to scale down the implementation cost with greater flexibility than other alternatives. However, we find that they both have overall complexities that scale linearly with the search space. Furthermore, it is shown that compressive measurements used in the SR-LRT lead to a performance loss when noise prevails, while providing better performance in spite of the compression when noise is mild.

Includes members:
  Member "asynchronous code division random access using convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on throughput of compressive random access for one short message delivery in iot")
  Member "gps signal acquisition via compressive multichannel sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compressive sensing for spread spectrum receivers")
  Member "reduced dimension multiuser detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an ialm ica based anti jamming ds cdma receiver for lms systems")
  Member "identification of parametric underspread linear systems and super resolution radar":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a general and yet efficient scheme for sub nyquist radar processing")
  Member "multichannel sampling of pulse streams at the rate of innovation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sub nyquist sar via fourier domain range doppler processing")
  Member "time delay estimation from low rate samples a union of subspaces approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "dynamic updating for ell_ 1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semi supervised sparse representation based classification for face recognition with insufficient labeled samples")
  Member "compressed sensing of analog signals in shift invariant spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit")

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)