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 "quickly finding the best linear model in high dimensions"):
  Abstract: We study the problem of finding the best linear model that can minimize least-squares loss given a data-set. While this problem is trivial in the low dimensional regime, it becomes more interesting in high dimensions where the population minimizer is assumed to lie on a manifold such as sparse vectors. We propose projected gradient descent (PGD) algorithm to estimate the population minimizer in the finite sample regime. We establish linear convergence rate and data dependent estimation error bounds for PGD. Our contributions include: 1) The results are established for heavier tailed sub-exponential distributions besides sub-gaussian. 2) We directly analyze the empirical risk minimization and do not require a realizable model that connects input data and labels. 3) Our PGD algorithm is augmented to learn the bias terms which boosts the performance. The numerical experiments validate our theoretical results.

Includes members:
  Member "quantized compressive sensing with rip matrices the benefit of dithering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "stochastic gradient descent learns state equations with nonlinear activations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems")
  Member "learning compact neural networks with regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems")
  Member "robust one bit compressed sensing with non gaussian measurements":
    also connected through other hyperedges:
      (none)
  Member "non gaussian hyperplane tessellations and robust one bit compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "robust one bit compressed sensing with partial circulant matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "the generalized lasso for sub gaussian measurements with dithered quantization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recovering structured data from superimposed non linear measurements")
  Member "one bit compressed sensing with partial gaussian circulant matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the importance of phase in complex compressive sensing")

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.LG(Machine Learning)