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: "a pac approach to application specific algorithm selection"
  Abstract: The best algorithm for a computational problem generally depends on the "relevant inputs," a concept that depends on the application domain and often defies formal articulation. While there is a large literature on empirical approaches to selecting the best algorithm for a given application domain, there has been surprisingly little theoretical analysis of the problem. #R##N#This paper adapts concepts from statistical and online learning theory to reason about application-specific algorithm selection. Our models capture several state-of-the-art empirical and theoretical approaches to the problem, ranging from self-improving algorithms to empirical performance models, and our results identify conditions under which these approaches are guaranteed to perform well. We present one framework that models algorithm selection as a statistical learning problem, and our work here shows that dimension notions from statistical learning theory, historically used to measure the complexity of classes of binary- and real-valued functions, are relevant in a much broader algorithmic context. We also study the online version of the algorithm selection problem, and give possibility and impossibility results for the existence of no-regret learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "learning complexity of simulated annealing"):
  with members:
    - "how much data is sufficient to learn high performing algorithms"
    - "learning to link"
    - "learning to branch"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"

Connected through hyperedge HE-2 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing"):
  with members:
    - "oboe collaborative filtering for automl model selection"
    - "convolutional neural networks with alternately updated clique"
    - "learning to branch"
    - "adabatch efficient gradient aggregation rules for sequential and parallel stochastic gradient methods"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning to learn by gradient descent by gradient descent"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "procrastinating with confidence near optimal anytime adaptive algorithm configuration"):
  with members:
    - "learning to branch"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "bandits with knapsacks"
    - "bayesian optimization with censored response data"

Connected through hyperedge HE-4 (induced by source paper "semi bandit optimization in the dispersed setting"):
  with members:
    - "data driven clustering via parameterized lloyd s families"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "online optimization of smoothed piecewise constant functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "k center clustering under perturbation resilience"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-5 (induced by source paper "learning to optimize computational resources frugal training with generalization guarantees"):
  with members:
    - "semi bandit optimization in the dispersed setting"
    - "learning to prune speeding up repeated computations"
    - "procrastinating with confidence near optimal anytime adaptive algorithm configuration"
    - "learning to link"
    - "learning to branch"
    - "learning robust search strategies using a bandit based approach"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"

Connected through hyperedge HE-6 (induced by source paper "learning optimal search algorithms from data"):
  with members:
    - "learning to prune speeding up repeated computations"
    - "the markovian price of information"
    - "learning to branch"
    - "competitive caching with machine learned advice"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "the price of information in combinatorial optimization"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "adaptivity gaps for stochastic probing submodular and xos functions"

Connected through hyperedge HE-7 (induced by source paper "learning to learn stochastic gradient descent with biased regularization"):
  with members:
    - "surrogate losses for online learning of stepsizes in stochastic non convex optimization"
    - "introduction to online convex optimization"
    - "fast rates by transferring from auxiliary hypotheses"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "sparse coding for multitask and transfer learning"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "learned frequency estimation algorithms under zipfian distribution"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to prune speeding up repeated computations"
    - "learning to branch"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "learning combinatorial optimization algorithms over graphs"
    - "a deep learning approach to structured signal recovery"

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)