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: "optimal algorithms for ridge and lasso regression with partially observed attributes"
  Abstract: We consider the most common variants of linear regression, including Ridge, Lasso and Support-vector regression, in a setting where the learner is allowed to observe only a fixed number of attributes of each example at training time. We present simple and efficient algorithms for these problems: for Lasso and Ridge regression they need the same total number of attributes (up to constants) as do full-information algorithms, for reaching a certain accuracy. For Support-vector regression, we require exponentially less attributes compared to the state of the art. By that, we resolve an open problem recently posed by Cesa-Bianchi et al. (2010). Experiments show the theoretical bounds to be justified by superior performance compared to the state of the art.

Connected through hyperedge HE-1 (induced by source paper "exploiting smoothness in statistical learning sequential prediction and stochastic optimization"):
  with members:
    - "binary excess risk for smooth convex surrogates"
    - "excess risk bounds for exponentially concave losses"
    - "passive learning with target risk"
    - "o logt projections for stochastic optimization of smooth and strongly convex functions"
    - "recovering the optimal solution by dual random projection"
    - "hybrid deterministic stochastic methods for data fitting"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "learning from an optimization viewpoint"

Connected through hyperedge HE-2 (induced by source paper "passive learning with target risk"):
  with members:
    - "learning from an optimization viewpoint"
    - "better mini batch algorithms via accelerated gradient methods"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "linear regression with limited observation"):
  with members:
    - "efficient learning with partially observed attributes"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-4 (induced by source paper "efficient learning with partially observed attributes"):
  with members:
    - "active model selection"
    - "online learning of noisy data with kernels"
    - "importance weighted active learning"

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)