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: "exact recovery conditions for sparse representations with partial support information"
  Abstract: We address the exact recovery of a k-sparse vector in the noiseless setting when some partial information on the support is available. This partial information takes the form of either a subset of the true support or an approximate subset including wrong atoms as well. We derive a new sufficient and worst-case necessary (in some sense) condition for the success of some procedures based on lp-relaxation, Orthogonal Matching Pursuit (OMP) and Orthogonal Least Squares (OLS). Our result is based on the coherence "mu" of the dictionary and relaxes the well-known condition mu<1/(2k-1) ensuring the recovery of any k-sparse vector in the non-informed setup. It reads mu<1/(2k-g+b-1) when the informed support is composed of g good atoms and b wrong atoms. We emphasize that our condition is complementary to some restricted-isometry based conditions by showing that none of them implies the other. #R##N#Because this mutual coherence condition is common to all procedures, we carry out a finer analysis based on the Null Space Property (NSP) and the Exact Recovery Condition (ERC). Connections are established regarding the characterization of lp-relaxation procedures and OMP in the informed setup. First, we emphasize that the truncated NSP enjoys an ordering property when p is decreased. Second, the partial ERC for OMP (ERC-OMP) implies in turn the truncated NSP for the informed l1 problem, and the truncated NSP for p<1.

Connected through hyperedge HE-1 (induced by source paper "an optimal restricted isometry condition for exact sparse recovery with orthogonal least squares"):
  with members:
    - "nearly optimal bounds for orthogonal least squares"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "support recovery with orthogonal matching pursuit in the presence of noise"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "rank awareness in joint sparse recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "a sharp recovery condition for sparse signals with partial support information via orthogonal matching pursuit"):
  with members:
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "recovery of signals under the high order rip condition via prior support information"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
    - "online recovery guarantees and analytical results for omp"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "on the theoretical analysis of orthogonal matching pursuit with termination based on the residue"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-3 (induced by source paper "insense incoherent sensor selection for sparse signals"):
  with members:
    - "near optimal sensor placement for linear inverse problems"
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "sparsity promoting sensor selection for non linear measurement models"
    - "online distributed sensor selection"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "on probability of support recovery for orthogonal matching pursuit using mutual coherence"):
  with members:
    - "on sparse representation in fourier and local bases"

Connected through hyperedge HE-5 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "multi terminal probabilistic compressed sensing"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal bounds for orthogonal least squares"):
  with members:
    - "on the exact recovery condition of simultaneous orthogonal matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "perturbation analysis of orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "compressed sensing with structured sparsity and structured acquisition"):
  with members:
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "on minimal trajectories for mobile sampling of bandlimited fields"
    - "sampling and reconstruction of spatial fields using mobile sensors"
    - "an algorithm for variable density sampling with block constrained acquisition"
    - "spread spectrum magnetic resonance imaging"
    - "performance bounds for grouped incoherent measurements in compressive sensing"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-8 (induced by source paper "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"):
  with members:
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "estimation of sparse mimo channels with common support"
    - "structured compressed sensing from theory to applications"

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)