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: "time invariant error bounds for modified cs based sparse signal sequence recovery"
  Abstract: In this work, we obtain performance guarantees for modified-CS and for its improved version, modified-CS-Add-LS-Del, for recursive reconstruction of a time sequence of sparse signals from a reduced set of noisy measurements available at each time. Under mild assumptions, we show that the support recovery error of both algorithms is bounded by a time-invariant and small value at all times. The same is also true for the reconstruction error. Under a slow support change assumption, (i) the support recovery error bound is small compared to the support size; and (ii) our results hold under weaker assumptions on the number of measurements than what $\ell_1$ minimization for noisy data needs. We first give a general result that only assumes a bound on support size, number of support changes and number of small magnitude nonzero entries at each time. Later, we specialize the main idea of these results for two sets of signal change assumptions that model the class of problems in which a new element that is added to the support either gets added at a large initial magnitude or its magnitude slowly increases to a large enough value within a finite delay. Simulation experiments are shown to back up our claims.

Connected through hyperedge HE-1 (induced by source paper "provable low rank phase retrieval and compressive pca"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "provable subspace tracking from missing data and matrix completion"
    - "phaseless pca low rank matrix recovery from column wise phaseless measurements"
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "sparse phase retrieval via truncated amplitude flow"

Connected through hyperedge HE-2 (induced by source paper "provable subspace tracking from missing data and matrix completion"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "subspace estimation from incomplete observations a high dimensional analysis"
    - "static and dynamic robust pca and matrix completion a review"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-3 (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"
    - "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"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-4 (induced by source paper "designing recurrent neural networks by unfolding an l1 l1 minimization algorithm"):
  with members:
    - "maximal sparsity with deep networks"
    - "unsupervised learning of video representations using lstms"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "how to construct deep recurrent neural networks"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-5 (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-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"
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

Connected through hyperedge HE-7 (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"
    - "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"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-8 (induced by source paper "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"):
  with members:
    - "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes"
    - "active sequential hypothesis testing"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks"
    - "sensor scheduling for energy efficient target tracking in sensor networks"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "cognitive interference management in retransmission based wireless networks"
    - "distributed compressive 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)