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: "active clustering robust and efficient hierarchical clustering using adaptively selected similarities"
  Abstract: Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small subset of pairwise similarities, significantly less than the complete set of N(N-1)/2 similarities. First, we show that if the intracluster similarities exceed intercluster similarities, then it is possible to correctly determine the hierarchical clustering from as few as 3N log N similarities. We demonstrate this order of magnitude savings in the number of pairwise similarities necessitates sequentially selecting which similarities to obtain in an adaptive fashion, rather than picking them at random. We then propose an active clustering method that is robust to a limited fraction of anomalous similarities, and show how even in the presence of these noisy similarity values we can resolve the hierarchical clustering using only O(N log^2 N) pairwise similarities.

Connected through hyperedge HE-1 (induced by source paper "interactive bayesian hierarchical clustering"):
  with members:
    - "local algorithms for interactive clustering"
    - "efficient active algorithms for hierarchical clustering"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-2 (induced by source paper "s2 an efficient graph based active learning algorithm with application to nonparametric classification"):
  with members:
    - "active learning on trees and graphs"

Connected through hyperedge HE-3 (induced by source paper "local algorithms for interactive clustering"):
  with members:
    - "clustering with same cluster queries"
    - "incremental clustering the case for extra clusters"
    - "robust hierarchical clustering"
    - "efficient active algorithms for hierarchical clustering"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-4 (induced by source paper "breaking the small cluster barrier of graph clustering"):
  with members:
    - "efficient active algorithms for hierarchical clustering"
    - "active learning using smooth relative regret approximations with applications"
    - "clustering partially observed graphs via convex optimization"
    - "nuclear norm minimization for the planted clique and biclique problems"

Connected through hyperedge HE-5 (induced by source paper "power to the points validating data memberships in clusterings"):
  with members:
    - "spatially aware comparison and consensus for clusterings"

Connected through hyperedge HE-6 (induced by source paper "clustering partially observed graphs via convex optimization"):
  with members:
    - "efficient clustering with limited distance information"
    - "robust hierarchical clustering"
    - "efficient active algorithms for hierarchical clustering"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "nuclear norm minimization for the planted clique and biclique problems"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-7 (induced by source paper "active learning using smooth relative regret approximations with applications"):
  with members:
    - "active learning on trees and graphs"
    - "active ranking using pairwise comparisons"
    - "agnostic active learning without constraints"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"
    - "importance weighted active learning"
    - "noisy sorting without resampling"
    - "correlation clustering with a fixed number of clusters"

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.IT(Information Theory)