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: "compressive demodulation of mutually interfering signals"
  Abstract: Multi-User Detection is fundamental not only to cellular wireless communication but also to Radio-Frequency Identification (RFID) technology that supports supply chain management. The challenge of Multi-user Detection (MUD) is that of demodulating mutually interfering signals, and the two biggest impediments are the asynchronous character of random access and the lack of channel state information. Given that at any time instant the number of active users is typically small, the promise of Compressive Sensing (CS) is the demodulation of sparse superpositions of signature waveforms from very few measurements. This paper begins by unifying two front-end architectures proposed for MUD by showing that both lead to the same discrete signal model. Algorithms are presented for coherent and noncoherent detection that are based on iterative matching pursuit. Noncoherent detection is all that is needed in the application to RFID technology where it is only the identity of the active users that is required. The coherent detector is also able to recover the transmitted symbols. It is shown that compressive demodulation requires $\mathcal{O}(K\log N(\tau+1))$ samples to recover $K$ active users whereas standard MUD requires $N(\tau+1)$ samples to process $N$ total users with a maximal delay $\tau$. Performance guarantees are derived for both coherent and noncoherent detection that are identical in the way they scale with number of active users. The power profile of the active users is shown to be less important than the SNR of the weakest user. Gabor frames and Kerdock codes are proposed as signature waveforms and numerical examples demonstrate the superior performance of Kerdock codes - the same probability of error with less than half the samples.

Connected through hyperedge HE-1 (induced by source paper "random access in c ran for user activity detection with limited capacity fronthaul"):
  with members:
    - "hybrid approximate message passing"
    - "q ary compressive sensing"
    - "message passing de quantization with applications to compressed sensing"
    - "hybrid approximate message passing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "hybrid approximate message passing with applications to structured sparsity"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "stable separation and super resolution of mixture models"):
  with members:
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "convexity in source separation models geometry and algorithms"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "asynchronous code division random access using convex optimization"
    - "compressed sensing off the grid"

Connected through hyperedge HE-3 (induced by source paper "super resolution of mutually interfering signals"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-4 (induced by source paper "robust spectral compressed sensing via structured matrix completion"):
  with members:
    - "incoherence optimal matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "spectral compressed sensing via structured matrix completion"
    - "near minimax line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "compressed sensing and matrix completion with constant proportion of corruptions"

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)