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: "low complexity multiclass encryption by compressed sensing"
  Abstract: The idea that compressed sensing may be used to encrypt information from unauthorized receivers has already been envisioned but never explored in depth since its security may seem compromised by the linearity of its encoding process. In this paper, we apply this simple encoding to define a general private-key encryption scheme in which a transmitter distributes the same encoded measurements to receivers of different classes, which are provided partially corrupted encoding matrices and are thus allowed to decode the acquired signal at provably different levels of recovery quality. The security properties of this scheme are thoroughly analyzed: first, the properties of our multiclass encryption are theoretically investigated by deriving performance bounds on the recovery quality attained by lower-class receivers with respect to high-class ones. Then, we perform a statistical analysis of the measurements to show that, although not perfectly secure, compressed sensing grants some level of security that comes at almost-zero cost and thus may benefit resource-limited applications. In addition to this, we report some exemplary applications of multiclass encryption by compressed sensing of speech signals, electrocardiographic tracks and images, in which quality degradation is quantified as the impossibility of some feature extraction algorithms to obtain sensitive information from suitably degraded signal recoveries.

Connected through hyperedge HE-1 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices"):
  with members:
    - "indistinguishability and energy sensitivity of asymptotically gaussian compressed encryption"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "bi level protected compressive sampling"
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "mergeshuffle a very fast parallel random permutation algorithm"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "a compressed sensing wire tap channel"

Connected through hyperedge HE-2 (induced by source paper "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "blind sensor calibration using approximate message passing"

Connected through hyperedge HE-3 (induced by source paper "fast reconstruction algorithm for perturbed compressive sensing based on total least squares and proximal splitting"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "bilinear generalized approximate message passing part i derivation"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "on the performance bound of sparse estimation with sensing matrix perturbation"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-4 (induced by source paper "bi level protected compressive sampling"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity cloud image privacy protection via matrix perturbation"
    - "joint quantization and diffusion for compressed sensing measurements of natural images"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "ripless compressed sensing from anisotropic measurements"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a probabilistic and ripless theory of compressed sensing"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"):
  with members:
    - "rakeness in the design of analog to information conversion of sparse and localized signals"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "robust coding of encrypted images via structural matrix"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "compressive measurements generated by structurally random matrices asymptotic normality and quantization"
    - "distribution of compressive measurements generated by structurally random matrices"
    - "fast and efficient compressive sensing using structurally random matrices"

Connected through hyperedge HE-7 (induced by source paper "bayesian de quantization and data compression for low energy physiological signal telemonitoring"):
  with members:
    - "troika a general framework for heart rate monitoring using wrist type photoplethysmographic signals during intensive physical exercise"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "compressed sensing for energy efficient wireless telemonitoring challenges and opportunities"
    - "energy efficient telemonitoring of physiological signals via compressed sensing a fast algorithm and power consumption evaluation"
    - "model based compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "towards secure compressive sampling scheme"):
  with members:
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity cloud image privacy protection via matrix perturbation"
    - "joint quantization and diffusion for compressed sensing measurements of natural images"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "embedding cryptographic features in compressive sensing"
    - "ripless compressed sensing from anisotropic measurements"
    - "permutation enhanced parallel reconstruction with a linear compressive sampling device"
    - "general deviants an analysis of perturbations in 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)