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: "compressive classification and the rare eclipse problem"
  Abstract: This paper addresses the fundamental question of when convex sets remain disjoint after random projection. We provide an analysis using ideas from high-dimensional convex geometry. For ellipsoids, we provide a bound in terms of the distance between these ellipsoids and simple functions of their polynomial coefficients. As an application, this theorem provides bounds for compressive classification of convex sets. Rather than assuming that the data to be classified is sparse, our results show that the data can be acquired via very few measurements yet will remain linearly separable. We demonstrate the feasibility of this approach in the context of hyperspectral imaging.

Connected through hyperedge HE-1 (induced by source paper "the rare eclipse problem on tiles quantised embeddings of disjoint convex sets"):
  with members:
    - "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"
    - "representation and coding of signal geometry"
    - "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-2 (induced by source paper "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"):
  with members:
    - "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"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"

Connected through hyperedge HE-3 (induced by source paper "small width low distortions quantized random embeddings of low complexity sets"):
  with members:
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "stable low rank matrix recovery via null space properties"
    - "on the gap between rip properties and sparse recovery conditions"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "representation and coding of signal geometry"):
  with members:
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "binary embedding fundamental limits and fast algorithm"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "a quantized johnson lindenstrauss lemma the finding of buffon s needle"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-5 (induced by source paper "discrimination on the grassmann manifold fundamental limits of subspace classifiers"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "learning robust subspace clustering"
    - "support recovery with sparsely sampled free random matrices"
    - "communications inspired linear discriminant analysis"
    - "on the capacity of large mimo block fading channels"
    - "approximate sparsity pattern recovery information theoretic lower bounds"

Connected through hyperedge HE-6 (induced by source paper "compressed sensing with side information geometrical interpretation and performance bounds"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "sharp mse bounds for proximal denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "various thresholds for ell_1 optimization 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.LG(Machine Learning)