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: "local algorithms for interactive clustering"
  Abstract: We study the design of interactive clustering algorithms for data sets satisfying natural stability assumptions. Our algorithms start with any initial clustering and only make local changes in each step; both are desirable features in many applications. We show that in this constrained setting one can still design provably efficient algorithms that produce accurate clusterings. We also show that our algorithms perform well on real-world data.

Connected through hyperedge HE-1 (induced by source paper "diameter based interactive structure discovery"):
  with members:
    - "diameter based active learning"
    - "a general framework for robust interactive learning"
    - "interactive bayesian hierarchical clustering"
    - "equality of opportunity in supervised learning"
    - "clustering with same cluster queries"
    - "efficient learning of linear separators under bounded noise"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "robust interactive learning"

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

Connected through hyperedge HE-3 (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-4 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "hierarchical clustering for euclidean data"
    - "hierarchical clustering with structural constraints"
    - "adaptive hierarchical clustering using ordinal queries"
    - "hierarchical clustering objective functions and algorithms"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "interactive bayesian hierarchical clustering"
    - "hierarchical clustering via spreading metrics"

Connected through hyperedge HE-5 (induced by source paper "flattening a hierarchical clustering through active learning"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "diameter based active learning"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "clustering with same cluster queries"
    - "efficient active learning of halfspaces an aggressive approach"
    - "agnostic active learning without constraints"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "importance weighted active learning"

Connected through hyperedge HE-6 (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"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "clustering under perturbation resilience"
    - "center based clustering under perturbation stability"

Connected through hyperedge HE-7 (induced by source paper "diameter based interactive structure search"):
  with members:
    - "a general framework for robust interactive learning"
    - "interactive bayesian hierarchical clustering"
    - "clustering with same cluster queries"
    - "efficient learning of linear separators under bounded noise"

Connected through hyperedge HE-8 (induced by source paper "interactive topic modeling with anchor words"):
  with members:
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"

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)