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 with multiple weighting sets"
  Abstract: In this paper, we study the support recovery conditions of weighted $\ell_1$ minimization for signal reconstruction from compressed sensing measurements when multiple support estimate sets with different accuracy are available. We identify a class of signals for which the recovered vector from $\ell_1$ minimization provides an accurate support estimate. We then derive stability and robustness guarantees for the weighted $\ell_1$ minimization problem with more than one support estimate. We show that applying a smaller weight to support estimate that enjoy higher accuracy improves the recovery conditions compared with the case of a single support estimate and the case with standard, i.e., non-weighted, $\ell_1$ minimization. Our theoretical results are supported by numerical simulations on synthetic signals and real audio signals.

Connected through hyperedge HE-1 (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"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "recovering compressively sampled signals using partial support information"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-2 (induced by source paper "weighted matrix completion and recovery with prior subspace information"):
  with members:
    - "what to expect when you are expecting on the grassmannian"
    - "streaming principal component analysis from incomplete data"
    - "mc 2 a two phase algorithm for leveraged matrix completion"
    - "incoherence optimal matrix completion"
    - "provable inductive matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "recovering compressively sampled signals using partial support information"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-3 (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"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-4 (induced by source paper "support driven reweighted ell_1 minimization"):
  with members:
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "sparse recovery by non convex optimization instance optimality"

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)