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 "on statistical learning of simplices unmixing problem revisited"):
  Abstract: Learning of a high-dimensional simplex from uniformly-sampled observations, generally known as the "unmixing problem", is a long-studied task in computer science. More recently, a significant interest is focused on this problem from other areas, such as computational biology and remote sensing. In this paper, we have studied the Probably Approximately Correct (PAC)-learnability of simplices in terms of sample complexity. Our analysis shows that a sufficient sample size for reliable learning of a $K$-simplex is only $O\left(K^2\log K\right)$, which yields a significant improvement over the current bound, i.e. $O\left(K^{22}\right)$. In particular, we propose a continuous-relaxation for Maximum Likelihood estimator which comes with a certificate of PAC-learnability, and is also followed by a scalable algorithm whose performance is extensively tested on synthetic and real-world datasets. Experimental results show that not only being comparable to rival methods on noiseless samples, our framework is superior to the state-of-the-art in noisy cases.

Includes members:
  Member "spectral unmixing via data guided sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low rank tensor grid for image completion")
  Member "efficient learning of simplices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding a latent k simplex in o k nnz data time via subset smoothing")

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)