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 general framework for robust interactive learning"
  Abstract: We propose a general framework for interactively learning models, such as (binary or non-binary) classifiers, orderings/rankings of items, or clusterings of data points. Our framework is based on a generalization of Angluin's equivalence query model and Littlestone's online learning model: in each iteration, the algorithm proposes a model, and the user either accepts it or reveals a specific mistake in the proposal. The feedback is correct only with probability $p > 1/2$ (and adversarially incorrect with probability $1 - p$), i.e., the algorithm must be able to learn in the presence of arbitrary noise. The algorithm's goal is to learn the ground truth model using few iterations. #R##N#Our general framework is based on a graph representation of the models and user feedback. To be able to learn efficiently, it is sufficient that there be a graph $G$ whose nodes are the models and (weighted) edges capture the user feedback, with the property that if $s, s^*$ are the proposed and target models, respectively, then any (correct) user feedback $s'$ must lie on a shortest $s$-$s^*$ path in $G$. Under this one assumption, there is a natural algorithm reminiscent of the Multiplicative Weights Update algorithm, which will efficiently learn $s^*$ even in the presence of noise in the user's feedback. #R##N#From this general result, we rederive with barely any extra effort classic results on learning of classifiers and a recent result on interactive clustering; in addition, we easily obtain new interactive learning algorithms for ordering/ranking.

Connected through hyperedge HE-1 (induced by source paper "diameter based interactive structure discovery"):
  with members:
    - "diameter based active 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"
    - "local algorithms for interactive clustering"
    - "robust interactive learning"

Connected through hyperedge HE-2 (induced by source paper "the complexity of interactively learning a stable matching by trial and error"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "deterministic and probabilistic binary search in graphs"
    - "inapproximability of nash equilibrium"
    - "a stable marriage requires communication"
    - "the complexity of non monotone markets"
    - "settling the complexity of arrow debreu equilibria in markets with additively separable utilities"

Connected through hyperedge HE-3 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms"):
  with members:
    - "binary search in graphs revisited"
    - "submodular optimization under noise"
    - "deterministic and probabilistic binary search in graphs"
    - "noisy sorting without resampling"

Connected through hyperedge HE-4 (induced by source paper "diameter based interactive structure search"):
  with members:
    - "interactive bayesian hierarchical clustering"
    - "clustering with same cluster queries"
    - "efficient learning of linear separators under bounded noise"
    - "local algorithms for interactive clustering"

Connected through hyperedge HE-5 (induced by source paper "simple and fast algorithms for interactive machine learning with random counter examples"):
  with members:
    - "local algorithms for interactive clustering"

Connected through hyperedge HE-6 (induced by source paper "a framework for searching in graphs in the presence of errors"):
  with members:
    - "binary search in graphs revisited"
    - "approximation strategies for generalized binary search in weighted trees"
    - "on the tree search problem with non uniform costs"
    - "deterministic and probabilistic binary search in graphs"
    - "local algorithms for interactive clustering"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "binary search in graphs revisited"):
  with members:
    - "adaptive hierarchical clustering using ordinal queries"
    - "approximation strategies for generalized binary search in weighted trees"
    - "searching on trees with noisy memory"
    - "deterministic and probabilistic binary search in graphs"

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)