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: "on partial sparse recovery"
  Abstract: We consider the problem of recovering a partially sparse solution of an underdetermined system of linear equations by minimizing the $\ell_1$-norm of the part of the solution vector which is known to be sparse. Such a problem is closely related to a classical problem in Compressed Sensing where the $\ell_1$-norm of the whole solution vector is minimized. We introduce analogues of restricted isometry and null space properties for the recovery of partially sparse vectors and show that these new properties are implied by their original counterparts. We show also how to extend recovery under noisy measurements to the partially sparse case.

Connected through hyperedge HE-1 (induced by source paper "on the exact recovery conditions of 3d human motion from 2d landmark motion with sparse articulated motion"):
  with members:
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end recovery of human shape and pose"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

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 "local sparsity and recovery of fusion frames structured signals"):
  with members:
    - "compressed sensing and parallel acquisition"
    - "compressed sensing with structured sparsity and structured acquisition"
    - "uniform recovery of fusion frame structured sparse signals"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "compressed sensing with general frames via optimal dual based ell _ 1 analysis"
    - "sparse recovery from combined fusion frame measurements"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-4 (induced by source paper "time invariant error bounds for modified cs based sparse signal sequence recovery"):
  with members:
    - "regularized modified bpdn for noisy sparse reconstruction with partial erroneous support and signal value knowledge"
    - "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-5 (induced by source paper "near optimal phase retrieval of sparse vectors"):
  with members:
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phase retrieval with polarization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "a proof of alon s second eigenvalue conjecture and related problems"

Connected through hyperedge HE-6 (induced by source paper "uniqueness conditions for a class of l0 minimization problems"):
  with members:
    - "rsp based analysis for sparsest and least ell_1 norm solutions to underdetermined linear systems"
    - "modified cs modifying compressive sensing for problems with partially known support"

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)