human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "compressive classification and the rare eclipse problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets")
  Member "small width low distortions quantized random embeddings of low complexity sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering")
  Member "binary embedding fundamental limits and fast algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices")
  Member "near optimal bounds for binary embeddings of arbitrary sets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one bit sensing of low rank and bisparse matrices")
  Member "dimensionality reduction with subgaussian matrices a unified theory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "a quantized johnson lindenstrauss lemma the finding of buffon s needle":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets")
  Member "new analysis of manifold embeddings and signal recovery from compressive measurements":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast and provable admm for learning with generative priors")

Question: Please tell me which class the source paper 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)