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: "on phase transition of compressed sensing in the complex domain"
  Abstract: The phase transition is a performance measure of the sparsity-undersampling tradeoff in compressed sensing (CS). This letter reports our first observation and evaluation of an empirical phase transition of the l1 minimization approach to the complex valued CS (CVCS), which is positioned well above the known phase transition of the real valued CS in the phase plane. This result can be considered as an extension of the existing phase transition theory of the block-sparse CS (BSCS) based on the universality argument, since the CVCS problem does not meet the condition required by the phase transition theory of BSCS but its observed phase transition coincides with that of BSCS. Our result is obtained by applying the recently developed ONE-L1 algorithms to the empirical evaluation of the phase transition of CVCS.

Connected through hyperedge HE-1 (induced by source paper "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"):
  with members:
    - "energy sustainable iot with individual qos constraints through miso swipt multicasting"
    - "sparse representation for wireless communications"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "compressive channel estimation and multi user detection in c ran"
    - "a random access protocol for pilot allocation in crowded massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "concise derivation of complex bayesian approximate message passing via expectation propagation"):
  with members:
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-3 (induced by source paper "variational bayesian algorithm for quantized compressed sensing"):
  with members:
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "bayesian compressed sensing with new sparsity inducing prior"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "one bit compressed sensing by linear programming"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "robustly stable signal recovery in compressed sensing with structured matrix perturbation"):
  with members:
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "orthonormal expansion ell_ 1 minimization algorithms for compressed sensing"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "compressed sensing how sharp is the restricted isometry property"
    - "model based compressive sensing"
    - "blind compressed sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "subsampling at information theoretically optimal rates"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "sub nyquist sampling of short pulses"
    - "the effect of spatial coupling on compressive sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "asymptotic analysis of complex lasso via complex approximate message passing camp"):
  with members:
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
    - "compressed sensing over ell_p balls minimax mean square error"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "minimum complexity pursuit"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"

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)