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 "adaptive feature selection computationally efficient online sparse linear regression under rip"):
  Abstract: Online sparse linear regression is an online problem where an algorithm repeatedly chooses a subset of coordinates to observe in an adversarially chosen feature vector, makes a real-valued prediction, receives the true label, and incurs the squared loss. The goal is to design an online learning algorithm with sublinear regret to the best sparse linear predictor in hindsight. Without any assumptions, this problem is known to be computationally intractable. In this paper, we make the assumption that data matrix satisfies restricted isometry property, and show that this assumption leads to computationally efficient algorithms with sublinear regret for two variants of the problem. In the first variant, the true label is generated according to a sparse linear model with additive Gaussian noise. In the second, the true label is chosen adversarially.

Includes members:
  Member "online sparse linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse regression via range counting")
  Member "greedy minimization of weakly supermodular set functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness")
  Member "learning without concentration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")
  Member "variable selection is hard":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate guarantees for dictionary learning")
  Member "linear regression with limited observation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online to offline conversions universality and adaptive minibatch sizes")
  Member "efficient learning with partially observed attributes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "classification with costly features as a sequential decision making problem")

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)