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: "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
  Abstract: We study the problem of sparse reconstruction from noisy undersampled measurements when the following knowledge is available. (1) We are given partial, and partly erroneous, knowledge of the signal's support, denoted by T . (2) We are also given an erroneous estimate of the signal values on T, denoted by (μ)T . In practice, both of these may be available from prior knowledge. Alternatively, in recursive reconstruction applications, like real-time dynamic MRI, one can use the support estimate and the signal value estimate from the previous time instant as T and (μ)T. In this paper, we introduce regularized modified basis pursuit denoising (BPDN) (reg-mod-BPDN) to solve this problem and obtain computable bounds on its reconstruction error. Reg-mod-BPDN tries to find the signal that is sparsest outside the set T, while being “close enough” to (μ)T on T and while satisfying the data constraint. Corresponding results for modified-BPDN and BPDN follow as direct corollaries. A second key contribution is an approach to obtain computable error bounds that hold without any sufficient conditions. This makes it easy to compare the bounds for the various approaches. Empirical reconstruction error comparisons with many existing approaches are also provided.

Connected through hyperedge HE-1 (induced by source paper "minimax optimal online stochastic learning for sequences of convex functions under sub gradient observation failures"):
  with members:
    - "introduction to online convex optimization"
    - "bandit convex optimization with biased noisy gradient oracles"
    - "scale free online learning"
    - "threshold effects in parameter estimation from compressed data"
    - "online learning with feedback graphs beyond bandits"
    - "minimax analysis of active learning"
    - "online and batch learning algorithms for data with missing features"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-2 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review"):
  with members:
    - "video compressive sensing for spatial multiplexing cameras using motion flow models"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "fpa cs focal plane array based compressive imaging in short wave infrared"
    - "quadratic basis pursuit"
    - "estimation of space time varying parameters using a diffusion lms algorithm"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "lensless imaging by compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "spectrum sharing radar coexistence via xampling"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "performance of time delay estimation in a cognitive radar"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "sub nyquist channel estimation over ieee 802 11ad link"
    - "on the co existence of td lte and radar over 3 5 ghz band an experimental study"
    - "spectrum sharing between a surveillance radar and secondary wi fi networks"
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"

Connected through hyperedge HE-4 (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"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

Connected through hyperedge HE-5 (induced by source paper "prior support knowledge aided sparse bayesian learning with partly erroneous support information"):
  with members:
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-6 (induced by source paper "time invariant error bounds for modified cs based sparse signal sequence recovery"):
  with members:
    - "on partial sparse recovery"
    - "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"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-7 (induced by source paper "pafimocs particle filtered modified cs and applications in visual tracking across illumination change"):
  with members:
    - "compressive acquisition of dynamic scenes"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-8 (induced by source paper "exact reconstruction conditions for regularized modified basis pursuit"):
  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"
    - "sparse signal reconstruction via iterative support detection"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kalman filtered compressed sensing"

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)