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: "regime change bit depth versus measurement rate in compressive sensing"
  Abstract: The recently introduced compressive sensing (CS) framework enables digital signal acquisition systems to take advantage of signal structures beyond bandlimitedness. Indeed, the number of CS measurements required for stable reconstruction is closer to the order of the signal complexity than the Nyquist rate. To date, the CS theory has focused on real-valued measurements, but in practice measurements are mapped to bits from a finite alphabet. Moreover, in many potential applications the total number of measurement bits is constrained, which suggests a tradeoff between the number of measurements and the number of bits per measurement. We study this situation in this paper and show that there exist two distinct regimes of operation that correspond to high/low signal-to-noise ratio (SNR). In the measurement compression (MC) regime, a high SNR favors acquiring fewer measurements with more bits per measurement; in the quantization compression (QC) regime, a low SNR favors acquiring more measurements with fewer bits per measurement. A surprise from our analysis and experiments is that in many practical applications it is better to operate in the QC regime, even acquiring as few as 1 bit per measurement.

Connected through hyperedge HE-1 (induced by source paper "quantized compressed sensing by rectified linear units"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "robust one bit compressed sensing with partial circulant matrices"
    - "on recovery guarantees for one bit compressed sensing on manifolds"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "methods for quantized compressed sensing"
    - "the generalized lasso with non linear observations"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"

Connected through hyperedge HE-2 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"

Connected through hyperedge HE-3 (induced by source paper "adaptive recovery of dictionary sparse signals using binary measurements"):
  with members:
    - "one bit compressive sensing of dictionary sparse signals"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-4 (induced by source paper "adaptive dictionary sparse signal recovery using binary measurements"):
  with members:
    - "massive mimo with 1 bit adc"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-5 (induced by source paper "image compression based on compressive sensing end to end comparison with jpeg"):
  with members:
    - "nonlocal low rank tensor factor analysis for image restoration"
    - "block wise lensless compressive camera"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "compressive hyperspectral imaging with side information"
    - "tree structure bayesian compressive sensing for video"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "feedback acquisition and reconstruction of spectrum sparse signals by predictive level comparisons"):
  with members:
    - "fast methods for recovering sparse parameters in linear low rank models"
    - "iterative methods for sparse signal reconstruction from level crossings"
    - "quantization of compressive samples with stable and robust recovery"
    - "sparse signal estimation by maximally sparse convex optimization"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-7 (induced by source paper "on the snr variability in noisy compressed sensing"):
  with members:
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "noise folding in compressed sensing"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "exit chart analysis of turbo compressed sensing using message passing dequantization"):
  with members:
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "iterative detection for compressive sensing turbo cs"
    - "joint source channel vector quantization for compressed sensing"
    - "message passing de quantization with applications to compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "message passing algorithms for compressed sensing"

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)