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 "active sample learning and feature selection a unified approach"):
  Abstract: This paper focuses on the problem of simultaneous sample and feature selection for machine learning in a fully unsupervised setting. Though most existing works tackle these two problems separately that derives two well-studied sub-areas namely active learning and feature selection, a unified approach is inspirational since they are often interleaved with each other. Noisy and high-dimensional features will bring adverse effect on sample selection, while `good' samples will be beneficial to feature selection. We present a unified framework to conduct active learning and feature selection simultaneously. From the data reconstruction perspective, both the selected samples and features can best approximate the original dataset respectively, such that the selected samples characterized by the selected features are very representative. Additionally our method is one-shot without iteratively selecting samples for progressive labeling. Thus our model is especially suitable when the initial labeled samples are scarce or totally absent, which existing works hardly address particularly for simultaneous feature selection. To alleviate the NP-hardness of the raw problem, the proposed formulation involves a convex but non-smooth optimization problem. We solve it efficiently by an iterative algorithm, and prove its global convergence. Experiments on publicly available datasets validate that our method is promising compared with the state-of-the-arts.

Includes members:
  Member "feature selection a data perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "optimal cur matrix decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "relative error cur matrix decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "active learning with statistical models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)