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: "representation and coding of signal geometry"
  Abstract: Approaches to signal representation and coding theory have traditionally focused on how to best represent signals using parsimonious representations that incur the lowest possible distortion. Classical examples include linear and non-linear approximations, sparse representations, and rate-distortion theory. Very often, however, the goal of processing is to extract specific information from the signal, and the distortion should be measured on the extracted information. The corresponding representation should, therefore, represent that information as parsimoniously as possible, without necessarily accurately representing the signal itself. #R##N#In this paper, we examine the problem of encoding signals such that sufficient information is preserved about their pairwise distances and their inner products. For that goal, we consider randomized embeddings as an encoding mechanism and provide a framework to analyze their performance. We also demonstrate that it is possible to design the embedding such that it represents different ranges of distances with different precision. These embeddings also allow the computation of kernel inner products with control on their inner product-preserving properties. Our results provide a broad framework to design and analyze embeddins, and generalize existing results in this area, such as random Fourier kernels and universal embeddings.

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 "instance optimal decoding and the restricted isometry property"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "small width low distortions quasi isometric embeddings with quantized sub gaussian random projections"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "quantized compressive k means"):
  with members:
    - "random projections through multiple optical scattering approximating kernels at the speed of light"
    - "compressive k means"
    - "random projections for k means clustering"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-4 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets"):
  with members:
    - "compressive classification and the rare eclipse problem"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "bounds on the number of measurements for reliable compressive classification"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "quantization and compressive sensing"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-5 (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"
    - "small width low distortions quantized random embeddings of low complexity sets"

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)