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: "sequential compressed sensing"
  Abstract: Compressed sensing allows perfect recovery of sparse signals (or signals sparse in some basis) using only a small number of random measurements. Existing results in compressed sensing literature have focused on characterizing the achievable performance by bounding the number of samples required for a given level of signal sparsity. However, using these bounds to minimize the number of samples requires a priori knowledge of the sparsity of the unknown signal, or the decay structure for near-sparse signals. Furthermore, there are some popular recovery methods for which no such bounds are known. In this paper, we investigate an alternative scenario where observations are available in sequence. For any recovery method, this means that there is now a sequence of candidate reconstructions. We propose a method to estimate the reconstruction error directly from the samples themselves, for every candidate in this sequence. This estimate is universal in the sense that it is based only on the measurement ensemble, and not on the recovery method or any assumed level of sparsity of the unknown signal. With these estimates, one can now stop observations as soon as there is reasonable certainty of either exact or sufficiently accurate reconstruction. They also provide a way to obtain ?run-time? guarantees for recovery methods that otherwise lack a priori performance bounds. We investigate both continuous (e.g., Gaussian) and discrete (e.g., Bernoulli) random measurement ensembles, both for exactly sparse and general near-sparse signals, and with both noisy and noiseless measurements.

Connected through hyperedge HE-1 (induced by source paper "uncertainty quantification of electronic and photonic ics with non gaussian correlated process variations"):
  with members:
    - "tensor computation a new framework for high dimensional problems in eda"
    - "enabling high dimensional hierarchical uncertainty quantification by anova and tensor train decomposition"
    - "stochastic testing simulator for integrated circuits and mems hierarchical and sparse techniques"
    - "calculation of generalized polynomial chaos basis functions and gauss quadrature rules in hierarchical uncertainty quantification"
    - "stochastic testing method for transistor level uncertainty quantification based on generalized polynomial chaos"

Connected through hyperedge HE-2 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "boolean compressed sensing and noisy group testing"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"

Connected through hyperedge HE-3 (induced by source paper "beam on graph simultaneous channel estimation for mmwave mimo systems with multiple users"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "race a rate adaptive channel estimation approach for millimeter wave mimo systems"
    - "fountain code inspired channel estimation for multi user millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse signal recovery using generalized approximate message passing with built in parameter estimation"
    - "massive mimo ten myths and one critical question"

Connected through hyperedge HE-4 (induced by source paper "beam on graph simultaneous channel estimation in multi user millimeter wave mimo systems"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "race a rate adaptive channel estimation approach for millimeter wave mimo systems"
    - "fountain code inspired channel estimation for multi user millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse signal recovery using generalized approximate message passing with built in parameter estimation"
    - "massive mimo ten myths and one critical question"

Connected through hyperedge HE-5 (induced by source paper "bayesian online compressed sensing"):
  with members:
    - "statistical mechanics approach to 1 bit compressed sensing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (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"
    - "sketching sparse matrices"
    - "communications inspired projection design with application to compressive sensing"
    - "compressive binary search"

Connected through hyperedge HE-7 (induced by source paper "improving imprecise compressive sensing models"):
  with members:
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "estimating unknown sparsity in compressed sensing"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "learning dynamic compressive sensing models"):
  with members:
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "estimating unknown sparsity in compressed sensing"
    - "model based compressive sensing"
    - "multi label prediction via 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)