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 theoretic foundations of algorithm configuration for combinatorial partitioning problems"
  Abstract: Max-cut, clustering, and many other partitioning problems that are of significant importance to machine learning and other scientific fields are NP-hard, a reality that has motivated researchers to develop a wealth of approximation algorithms and heuristics. Although the best algorithm to use typically depends on the specific application domain, a worst-case analysis is often used to compare algorithms. This may be misleading if worst-case instances occur infrequently, and thus there is a demand for optimization methods which return the algorithm configuration best suited for the given application's typical inputs. We address this problem for clustering, max-cut, and other partitioning problems, such as integer quadratic programming, by designing computationally efficient and sample efficient learning algorithms which receive samples from an application-specific distribution over problem instances and learn a partitioning algorithm with high expected performance. Our algorithms learn over common integer quadratic programming and clustering algorithm families: SDP rounding algorithms and agglomerative clustering algorithms with dynamic programming. For our sample complexity analysis, we provide tight bounds on the pseudodimension of these algorithm classes, and show that surprisingly, even for classes of algorithms parameterized by a single parameter, the pseudo-dimension is superconstant. In this way, our work both contributes to the foundations of algorithm configuration and pushes the boundaries of learning theory, since the algorithm classes we analyze consist of multi-stage optimization procedures and are significantly more complex than classes typically studied in learning theory.

Connected through hyperedge HE-1 (induced by source paper "learning piecewise lipschitz functions in changing environments"):
  with members:
    - "introduction to online convex optimization"
    - "data driven clustering via parameterized lloyd s families"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "online optimization of smoothed piecewise constant functions"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "an algorithm for online k means clustering"
    - "mirror descent meets fixed share and feels no regret"

Connected through hyperedge HE-2 (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"
    - "a pac approach to application specific algorithm selection"

Connected through hyperedge HE-3 (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-4 (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"
    - "a pac approach to application specific algorithm selection"
    - "bandits with knapsacks"
    - "bayesian optimization with censored response data"

Connected through hyperedge HE-5 (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"
    - "k center clustering under perturbation resilience"
    - "a pac approach to application specific algorithm selection"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "learning to link"):
  with members:
    - "how much data is sufficient to learn high performing algorithms"
    - "data driven clustering via parameterized lloyd s families"
    - "a pac approach to application specific algorithm selection"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "local algorithms for interactive clustering"
    - "clustering under perturbation resilience"
    - "center based clustering under perturbation stability"

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.DS(Data Structures and Algorithms)