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: "sketching sparse matrices"
  Abstract: This paper considers the problem of recovering an unknown sparse p⇥ p matrix X from an m ⇥ m matrix Y = AXB T ,w hereA and B are known m ⇥ p matrices with m ⌧ p. The main result shows that there exist constructions of the “sketching” matrices A and B so that even if X has O(p) non-zeros, it can be recovered exactly and eciently using a convex program as long as these non-zeros are not concentrated in any single row/column of X. Furthermore, it suces for the size of Y (the sketch dimension) to scale as m = O p # nonzeros in X ⇥ logp . The results also show that the recovery is robust and stable in the sense that if X is equal to a sparse matrix plus a perturbation, then the convex program we propose produces an approximation with accuracy proportional to the size of the perturbation. Unlike traditional results on sparse recovery, where the sensing matrix produces independent measurements, our sensing operator is highly constrained (it assumes a tensor product structure). Therefore, proving recovery guarantees require non-standard techniques. Indeed our approach relies on a novel result concerning tensor products of bipartite graphs, which may be of independent interest. This problem is motivated by the following application, among others. Consider a p ⇥ n data matrix D, consisting of n observations of p variables. Assume that the correlation matrix X := DD T is (approximately) sparse in the sense that each of the p variables is significantly correlated with only a few others. Our results show that these significant correlations can be detected even if we have access to only a sketch of the data S = AD with A 2 R m⇥p .

Connected through hyperedge HE-1 (induced by source paper "hierarchical restricted isometry property for kronecker product measurements"):
  with members:
    - "low overhead hierarchically sparse channel estimation for multiuser wideband massive mimo"
    - "secure massive iot using hierarchical fast blind deconvolution"
    - "performance of hierarchical sparse detectors for massive mtc"
    - "hierarchical sparse channel estimation for massive mimo"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive random access using a common overloaded control channel"

Connected through hyperedge HE-2 (induced by source paper "positive definite estimation of large covariance matrix using generalized nonconvex penalties"):
  with members:
    - "massive mimo as a big data system random matrix models and testbed"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "recovery of sparse matrices via matrix sketching"

Connected through hyperedge HE-3 (induced by source paper "sequential information guided sensing"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "communications inspired projection design with application to compressive sensing"
    - "compressive binary search"
    - "sequential compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "exact and stable covariance estimation from quadratic sampling via convex programming"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "robust spectral compressed sensing via structured matrix completion"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"

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)