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 "compressed sensing in hilbert spaces"):
  Abstract: In many linear inverse problems, we want to estimate an unknown vector belonging to a high-dimensional (or infinite-dimensional) space from few linear measurements. To overcome the ill-posed nature of such problems, we use a low-dimension assumption on the unknown vector: it belongs to a low-dimensional model set. The question of whether it is possible to recover such an unknown vector from few measurements then arises. If the answer is yes, it is also important to be able to describe a way to perform such a recovery. We describe a general framework where appropriately chosen random measurements guarantee that recovery is possible. We further describe a way to study the performance of recovery methods that consist in the minimization of a regularization function under a data-fit constraint.

Includes members:
  Member "sketching for large scale learning of mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "compressive k means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "recipes for stable linear embeddings from hilbert spaces to ℝ m":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "functional nonlinear sparse models")
  Member "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the importance of phase in complex compressive sensing")
  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 "fundamental performance limits for ideal decoders in high dimensional linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem")
  Member "uniform recovery of fusion frame structured sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the importance of phase in complex compressive sensing")
  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)