human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "tree search network for sparse regression"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep sparse coding using optimized linear expansion of thresholds"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "admm net a deep learning approach for compressive sensing mri"
    - "distributed compressive sensing a deep learning approach"
    - "greedy subspace pursuit for joint sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"):
  with members:
    - "optimal pricing for submodular valuations with bounded curvature"
    - "near optimal bayesian active learning for decision making"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Question: Please tell me which class the center node 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)