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: "linear regression with limited observation"
  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 "online to offline conversions universality and adaptive minibatch sizes"):
  with members:
    - "the power of normalization faster evasion of saddle points"
    - "beyond convexity stochastic quasi convex optimization"
    - "distributed mini batch sdca"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"
    - "better mini batch algorithms via accelerated gradient methods"
    - "adaptive bound optimization for online convex optimization"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-2 (induced by source paper "coupled compound poisson factorization"):
  with members:
    - "hierarchical compound poisson factorization"
    - "classification with low rank and missing data"
    - "scalable recommendation with poisson factorization"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
    - "efficient learning with partially observed attributes"

Connected through hyperedge HE-3 (induced by source paper "adaptive feature selection computationally efficient online sparse linear regression under rip"):
  with members:
    - "online sparse linear regression"
    - "greedy minimization of weakly supermodular set functions"
    - "learning without concentration"
    - "variable selection is hard"
    - "efficient learning with partially observed attributes"

Connected through hyperedge HE-4 (induced by source paper "learning infinite layer networks without the kernel trick"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "steps toward deep kernel methods from infinite neural networks"
    - "on the complexity of learning with kernels"
    - "the sample complexity of learning linear predictors with the squared loss"
    - "scalable kernel methods via doubly stochastic gradients"
    - "convolutional kernel networks"

Connected through hyperedge HE-5 (induced by source paper "online sparse linear regression"):
  with members:
    - "variable selection is hard"
    - "analytical approach to parallel repetition"
    - "efficient learning with partially observed attributes"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-6 (induced by source paper "classification with low rank and missing data"):
  with members:
    - "introduction to online convex optimization"
    - "near optimal algorithms for online matrix prediction"
    - "efficient learning with partially observed attributes"
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "an equivalence between the lasso and support vector machines"):
  with members:
    - "sublinear time measurement optimal sparse recovery for all"
    - "efficient learning with partially observed attributes"
    - "safe feature elimination for the lasso and sparse supervised learning problems"
    - "sublinear optimization for machine learning"
    - "an exponential lower bound on the complexity of regularization paths"

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)