human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adaptive hierarchical clustering using ordinal queries"):
  Abstract: In many applications of clustering (for example, ontologies or clusterings of animal or plant species), hierarchical clusterings are more descriptive than a flat clustering. A hierarchical clustering over $n$ elements is represented by a rooted binary tree with $n$ leaves, each corresponding to one element. The subtrees rooted at interior nodes capture the clusters. In this paper, we study active learning of a hierarchical clustering using only ordinal queries. An ordinal query consists of a set of three elements, and the response to a query reveals the two elements (among the three elements in the query) which are "closer" to each other than to the third one. We say that elements $x$ and $x'$ are closer to each other than $x"$ if there exists a cluster containing $x$ and $x'$, but not $x"$. #R##N#When all the query responses are correct, there is a deterministic algorithm that learns the underlying hierarchical clustering using at most $n \log_2 n$ adaptive ordinal queries. We generalize this algorithm to be robust in a model in which each query response is correct independently with probability $p > \frac{1}{2}$, and adversarially incorrect with probability $1 - p$. We show that in the presence of noise, our algorithm outputs the correct hierarchical clustering with probability at least $1 - \delta$, using $O(n \log n + n \log(1/\delta))$ adaptive ordinal queries. For our results, adaptivity is crucial: we prove that even in the absence of noise, every non-adaptive algorithm requires $\Omega(n^3)$ ordinal queries in the worst case.

Includes members:
  Member "interactive bayesian hierarchical clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "diameter based interactive structure discovery")
  Member "a cost function for similarity based hierarchical clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical correlation clustering and tree preserving embedding")
  Member "deterministic and probabilistic binary search in graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents")
  Member "local algorithms for interactive clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diameter based interactive structure discovery")
  Member "active ranking using pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning from noisy similar and dissimilar data")
  Member "adaptively learning the crowd kernel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpretable off policy evaluation in reinforcement learning by highlighting influential transitions")
  Member "learning multi modal similarity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a similarity measure for material appearance")
  Member "noisy sorting without resampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "searching trees with permanently noisy advice walking and query algorithms")

Question: Please tell me which class the source paper 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)