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: "frame permutation quantization"
  Abstract: Abstract   Frame permutation quantization (FPQ) is a new vector quantization technique using finite frames. In FPQ, a vector is encoded using a permutation source code to quantize its frame expansion. This means that the encoding is a partial ordering of the frame expansion coefficients. Compared to ordinary permutation source coding, FPQ produces a greater number of possible quantization rates and a higher maximum rate. Various representations for the partitions induced by FPQ are presented, and reconstruction algorithms based on linear programming, quadratic programming, and recursive orthogonal projection are derived. Implementations of the linear and quadratic programming algorithms for uniform and Gaussian sources show performance improvements over entropy-constrained scalar quantization for certain combinations of vector dimension and coding rate. Monte Carlo evaluation of the recursive algorithm shows that mean-squared error (MSE) decays as     M   −  4      for an M-element frame, which is consistent with previous results on optimal decay of MSE. Reconstruction using the canonical dual frame is also studied, and several results relate properties of the analysis frame to whether linear reconstruction techniques provide consistent reconstructions.

Connected through hyperedge HE-1 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering"):
  with members:
    - "instance optimal decoding and the restricted isometry property"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-2 (induced by source paper "taking the edge off quantization projected back projection in dithered compressive sensing"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "isometric sketching of any set via the restricted isometry property"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "the generalized lasso with non linear observations"

Connected through hyperedge HE-3 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"

Connected through hyperedge HE-4 (induced by source paper "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections"
    - "on the gap between rip properties and sparse recovery conditions"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "quantization and compressive sensing"
    - "unlocbox a matlab convex optimization toolbox using proximal splitting methods"
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"

Connected through hyperedge HE-5 (induced by source paper "optimizing quantization for lasso recovery"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "methods for quantized compressed sensing"
    - "the generalized lasso with non linear observations"
    - "quantization and compressive sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "message passing de quantization with applications to compressed sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

Connected through hyperedge HE-6 (induced by source paper "quantization and compressive sensing"):
  with members:
    - "error decay of almost consistent signal estimations from quantized random gaussian projections"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "near optimal encoding for sigma delta quantization of finite frame expansions"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"

Connected through hyperedge HE-7 (induced by source paper "consistent signal and matrix estimates in quantized compressed sensing"):
  with members:
    - "quantization and compressive sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "message passing de quantization with applications to compressed sensing"
    - "one bit compressed sensing with non gaussian measurements"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-8 (induced by source paper "error decay of almost consistent signal estimations from quantized gaussian random projections"):
  with members:
    - "quantization and compressive sensing"
    - "error bounds for consistent reconstruction random polytopes and coverage processes"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "message passing de quantization with applications to compressed sensing"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "sobolev duals for random frames and sigma delta quantization of compressed sensing measurements"

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)