human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "gps signal acquisition via compressive multichannel sampling"
  Abstract: In this paper, we propose an efficient acquisition scheme for GPS receivers. It is shown that GPS signals can be effectively sampled and detected using a bank of randomized correlators with much fewer chip-matched filters than those used in existing GPS signal acquisition algorithms. The latter use correlations with all possible shifted replicas of the satellite-specific C/A code and an exhaustive search for peaking signals over the delay-Doppler space. Our scheme is based on the recently proposed analog compressed sensing framework, and consists of a multichannel sampling structure with far fewer correlators. #R##N#The compressive multichannel sampler outputs are linear combinations of a vector whose support tends to be sparse; by detecting its support one can identify the strongest satellite signals in the field of view and pinpoint the correct code-phase and Doppler shifts for finer resolution during tracking. The analysis in this paper demonstrates that GPS signals can be detected and acquired via the proposed structure at a lower cost in terms of number of correlations that need to be computed in the coarse acquisition phase, which in current GPS technology scales like the product of the number of all possible delays and Doppler shifts. In contrast, the required number of correlators in our compressive multichannel scheme scales as the number of satellites in the field of view of the device times the logarithm of number of delay-Doppler bins explored, as is typical for compressed sensing methods.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing for spread spectrum receivers"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "reduced dimension multiuser detection"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "noise folding in compressed sensing"
    - "demodulating subsampled direct sequence spread spectrum signals using compressive signal processing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "compressed sensing how sharp is the restricted isometry property"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "compressive link acquisition in multiuser communications"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "reduced dimension multiuser detection"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "dynamic updating for ell_ 1 minimization"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "on off random access channels a compressed sensing framework"

Connected through hyperedge HE-3 (induced by source paper "support recovery of sparse signals in the presence of multiple measurement vectors"):
  with members:
    - "support recovery of sparse signals"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-4 (induced by source paper "demodulating subsampled direct sequence spread spectrum signals using compressive signal processing"):
  with members:
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Question: Please tell me which class the center node 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)