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: "weighted ell_1 minimization for sparse recovery with prior information"
  Abstract: In this paper we study the compressed sensing problem of recovering a sparse signal from a system of underdetermined linear equations when we have prior information about the probability of each entry of the unknown signal being nonzero. In particular, we focus on a model where the entries of the unknown vector fall into two sets, each with a different probability of being nonzero. We propose a weighted $\ell_1$ minimization recovery algorithm and analyze its performance using a Grassman angle approach. We compute explicitly the relationship between the system parameters (the weights, the number of measurements, the size of the two sets, the probabilities of being non-zero) so that an iid random Gaussian measurement matrix along with weighted $\ell_1$ minimization recovers almost all such sparse signals with overwhelming probability as the problem dimension increases. This allows us to compute the optimal weights. We also provide simulations to demonstrate the advantages of the method over conventional $\ell_1$ optimization.

Connected through hyperedge HE-1 (induced by source paper "deterministic analysis of weighted bpdn with partially known support information"):
  with members:
    - "signal recovery under cumulative coherence"
    - "recovery of signals under the condition on ric and roc via prior support information"
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-2 (induced by source paper "rls recovery with asymmetric penalty fundamental limits and algorithmic approaches"):
  with members:
    - "precoding via approximate message passing with instantaneous signal constraints"
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "rsb decoupling property of map estimators"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "statistical mechanics of map estimation general replica ansatz"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-3 (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"
    - "exact recovery conditions for sparse representations with partial support information"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "maximal sparsity with deep networks"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "accelerating eulerian fluid simulation with convolutional networks"
    - "sampling stationary signals subject to bitrate constraints"
    - "raisr rapid and accurate image super resolution"
    - "fundamental distortion limits of analog to digital compression"
    - "a picture is worth a billion bits real time image reconstruction from dense binary pixels"

Connected through hyperedge HE-5 (induced by source paper "stft phase retrieval uniqueness guarantees and recovery algorithms"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval an overview of recent developments"
    - "sparse phase retrieval from short time fourier measurements"
    - "quadratic basis pursuit"
    - "phase retrieval with application to optical imaging"
    - "phase retrieval for sparse signals uniqueness conditions"
    - "gespar efficient phase retrieval of sparse signals"

Connected through hyperedge HE-6 (induced by source paper "performance bounds for sparse signal reconstruction with multiple side information"):
  with members:
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "adaptive rate compressive sensing using side information"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressed sensing with side information geometrical interpretation and performance bounds"
    - "robust pca with partial subspace knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"

Connected through hyperedge HE-7 (induced by source paper "weighted ell_1 minimization for sparse recovery under arbitrary prior information"):
  with members:
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "exact recovery for sparse signal via weighted l_1 minimization"
    - "on the theorem of uniform recovery of random sampling matrices"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "recovering compressively sampled signals using partial support information"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "recovery of signals under the condition on ric and roc via prior support information"):
  with members:
    - "recovery of signals under the high order rip condition via prior support information"
    - "recovery analysis for weighted ell_1 minimization using a null space property"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "time invariant error bounds for modified cs based sparse signal sequence recovery"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "weighted ell_1 minimization with multiple weighting sets"
    - "recovering compressively sampled signals using partial support information"

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)