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: "learning from an optimization viewpoint"
  Abstract: In this dissertation we study statistical and online learning problems from an optimization viewpoint.The dissertation is divided into two parts : #R##N#I. We first consider the question of learnability for statistical learning problems in the general learning setting. The question of learnability is well studied and fully characterized for binary classification and for real valued supervised learning problems using the theory of uniform convergence. However we show that for the general learning setting uniform convergence theory fails to characterize learnability. To fill this void we use stability of learning algorithms to fully characterize statistical learnability in the general setting. Next we consider the problem of online learning. Unlike the statistical learning framework there is a dearth of generic tools that can be used to establish learnability and rates for online learning problems in general. We provide online analogs to classical tools from statistical learning theory like Rademacher complexity, covering numbers, etc. We further use these tools to fully characterize learnability for online supervised learning problems. #R##N#II. In the second part, for general classes of convex learning problems, we provide appropriate mirror descent (MD) updates for online and statistical learning of these problems. Further, we show that the the MD is near optimal for online convex learning and for most cases, is also near optimal for statistical convex learning. We next consider the problem of convex optimization and show that oracle complexity can be lower bounded by the so called fat-shattering dimension of the associated linear class. Thus we establish a strong connection between offline convex optimization problems and statistical learning problems. We also show that for a large class of high dimensional optimization problems, MD is in fact near optimal even for convex optimization.

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"
    - "minimizing the misclassification error rate using a surrogate convex loss"

Connected through hyperedge HE-2 (induced by source paper "breaking the curse of dimensionality with convex neural networks"):
  with members:
    - "on the equivalence between quadrature rules and random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the computational efficiency of training neural networks"
    - "computational aspects of the hausdorff distance in unbounded dimension"
    - "convex relaxations of structured matrix factorizations"
    - "duality between subgradient and conditional gradient methods"
    - "exploring large feature spaces with hierarchical multiple kernel learning"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-3 (induced by source paper "passive learning with target risk"):
  with members:
    - "optimal algorithms for ridge and lasso regression with partially observed attributes"
    - "better mini batch algorithms via accelerated gradient methods"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "stochastic optimization for machine learning"):
  with members:
    - "the kernelized stochastic batch perceptron"
    - "on the universality of online mirror descent"

Connected through hyperedge HE-5 (induced by source paper "online stochastic optimization with multiple objectives"):
  with members:
    - "multi criteria anomaly detection using pareto depth analysis"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "trading regret for efficiency online convex optimization with long term constraints"

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)