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: "modified basis pursuit denoising modified bpdn for noisy compressive sensing with partially known support"
  Abstract: In this work, we study the problem of reconstructing a sparse signal from a limited number of linear 'incoherent' noisy measurements, when a part of its support is known. The known part of the support may be available from prior knowledge or from the previous time instant (in applications requiring recursive reconstruction of a time sequence of sparse signals, e.g. dynamic MRI). We study a modification of Basis Pursuit Denoising (BPDN) and bound its reconstruction error. A key feature of our work is that the bounds that we obtain are computable. Hence, we are able to use Monte Carlo to study their average behavior as the size of the unknown support increases. We also demonstrate that when the unknown support size is small, modified-BPDN bounds are much tighter than those for BPDN, and hold under much weaker sufficient conditions (require fewer measurements).

Connected through hyperedge HE-1 (induced by source paper "recovery of signals under the high order rip condition via prior support information"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "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"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-2 (induced by source paper "time invariant error bounds for modified cs based sparse signal sequence recovery"):
  with members:
    - "on partial sparse recovery"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "subspace methods for joint sparse recovery"
    - "exact dynamic support tracking with multiple measurement vectors using compressive music"
    - "support predicted modified cs for recursive robust principal components pursuit"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse signal reconstruction via iterative support detection"

Connected through hyperedge HE-3 (induced by source paper "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"):
  with members:
    - "reprocs a missing link between recursive robust pca and recursive sparse recovery in large but correlated noise"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "modified orthogonal matching pursuit algorithm for cognitive radio wideband spectrum sensing"):
  with members:
    - "compressive wideband spectrum sensing for fixed frequency spectrum allocation"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "modified cs modifying compressive sensing for problems with partially known support"):
  with members:
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "stability over time of modified cs and ls cs for recursive causal sparse reconstruction"
    - "weighted ell_1 minimization for sparse recovery with prior information"
    - "analyzing least squares and kalman filtered compressed sensing"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "real time robust principal components pursuit"):
  with members:
    - "dense error correction for low rank matrices via principal component pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "robust principal component analysis"
    - "dense error correction via l1 minimization"

Connected through hyperedge HE-7 (induced by source paper "recovering compressively sampled signals using partial support information"):
  with members:
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior 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)