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: "an algorithm for variable density sampling with block constrained acquisition"
  Abstract: Reducing acquisition time is of fundamental importance in various imaging modalities. The concept of variable density sampling provides a nice framework to achieve this. It was justified recently from a theoretical point of view in the compressed sensing (CS) literature. Unfortunately, the sampling schemes suggested by current CS theories may not be relevant since they do not take the acquisition constraints into account (for example, continuity of the acquisition trajectory in Magnetic Resonance Imaging - MRI). In this paper, we propose a numerical method to perform variable density sampling with block constraints. Our main contribution is to propose a new way to draw the blocks in order to mimic CS strategies based on isolated measurements. The basic idea is to minimize a tailored dissimilarity measure between a probability distribution defined on the set of isolated measurements and a probability distribution defined on a set of blocks of measurements. This problem turns out to be convex and solvable in high dimension. Our second contribution is to define an efficient minimization algorithm based on Nesterov's accelerated gradient descent in metric spaces. We study carefully the choice of the metrics and of the prox function. We show that the optimal choice may depend on the type of blocks under consideration. Finally, we show that we can obtain better MRI reconstruction results using our sampling schemes than standard strategies such as equiangularly distributed radial lines.

Connected through hyperedge HE-1 (induced by source paper "on oracle type local recovery guarantees in compressed sensing"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "compressed sensing with local structure uniform recovery guarantees for the sparsity in levels class"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "living on the edge phase transitions in convex programs with random data"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "on variable density compressive sampling"

Connected through hyperedge HE-2 (induced by source paper "signal reconstruction from interferometric measurements under sensing constraints"):
  with members:
    - "on the 2d phase retrieval problem"
    - "an analysis of block sampling strategies in compressed sensing"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "compressed sensing and parallel acquisition"):
  with members:
    - "sparsity and parallel acquisition optimal uniform and nonuniform recovery guarantees"
    - "optimal sparse recovery for multi sensor measurements"
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "self calibration and biconvex compressive sensing"
    - "refined analysis of sparse mimo radar"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "multi view in lensless compressive imaging"
    - "wireless compressive sensing for energy harvesting sensor nodes"

Connected through hyperedge HE-4 (induced by source paper "learning based compressive subsampling"):
  with members:
    - "a totally unimodular view of structured sparsity"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "group sparse model selection hardness and relaxations"
    - "energy aware adaptive bi lipschitz embeddings"
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "combinatorial selection and least absolute shrinkage via the clash algorithm"
    - "on variable density compressive sampling"
    - "model based compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "compressed sensing with structured sparsity and structured acquisition"):
  with members:
    - "on the absence of the rip in real world applications of compressed sensing and the rip in levels"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "on minimal trajectories for mobile sampling of bandlimited fields"
    - "sampling and reconstruction of spatial fields using mobile sensors"
    - "exact recovery conditions for sparse representations with partial support information"
    - "spread spectrum magnetic resonance imaging"
    - "performance bounds for grouped incoherent measurements in compressive sensing"
    - "stable and robust sampling strategies for compressive imaging"

Connected through hyperedge HE-6 (induced by source paper "on asymptotic incoherence and its implications for compressed sensing of inverse problems"):
  with members:
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "stable and robust sampling strategies for compressive imaging"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "an analysis of block sampling strategies in compressed sensing"):
  with members:
    - "compressed sensing with structured sparsity and structured acquisition"
    - "a partial derandomization of phaselift using spherical designs"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "spread spectrum magnetic resonance imaging"
    - "performance bounds for grouped incoherent measurements in compressive sensing"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "stable and robust sampling strategies for compressive imaging"
    - "recovering low rank matrices from few coefficients in any basis"

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)