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: "compressed sensing with nonlinear observations and related nonlinear optimisation problems"
  Abstract: Non-convex constraints have recently proven a valuable tool in many optimisation problems. In particular sparsity constraints have had a significant impact on sampling theory, where they are used in Compressed Sensing and allow structured signals to be sampled far below the rate traditionally prescribed. #R##N#Nearly all of the theory developed for Compressed Sensing signal recovery assumes that samples are taken using linear measurements. In this paper we instead address the Compressed Sensing recovery problem in a setting where the observations are non-linear. We show that, under conditions similar to those required in the linear setting, the Iterative Hard Thresholding algorithm can be used to accurately recover sparse or structured signals from few non-linear observations. #R##N#Similar ideas can also be developed in a more general non-linear optimisation framework. In the second part of this paper we therefore present related result that show how this can be done under sparsity and union of subspaces constraints, whenever a generalisation of the Restricted Isometry Property traditionally imposed on the Compressed Sensing system holds.

Connected through hyperedge HE-1 (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-2 (induced by source paper "quadratic basis pursuit"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "gespar efficient phase retrieval of sparse signals"
    - "on conditions for uniqueness in sparse phase retrieval"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval stability and recovery guarantees"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-3 (induced by source paper "nonlinear basis pursuit"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "quadratic basis pursuit"
    - "gespar efficient phase retrieval of sparse signals"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"
    - "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "chaotic analog to information conversion with chaotic state modulation"):
  with members:
    - "a compressed sensing framework of frequency sparse signals through chaotic system"
    - "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"
    - "segmented compressed sampling for analog to information conversion method and performance analysis"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "nonlinear compressive particle filtering"):
  with members:
    - "quadratic basis pursuit"
    - "pafimocs particle filtered modified cs and applications in visual tracking across illumination change"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "kalman filtered compressed sensing"
    - "particle filtering for large dimensional state spaces with multimodal observation likelihoods"

Connected through hyperedge HE-6 (induced by source paper "chaotic analog to information conversion principle and reconstructability with parameter identifiability"):
  with members:
    - "a compressed sensing framework of frequency sparse signals through chaotic system"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

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)