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: "efficient clustering with limited distance information"
  Abstract: Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points. In our model we assume that we have access to one versus all queries that given a point s in S return the distances between s and all other points. We show that given a natural assumption about the structure of the instance, we can efficiently find an accurate clustering using only O(k) distance queries. Our algorithm uses an active selection strategy to choose a small set of points that we call landmarks, and considers only the distances between landmarks and other points to produce a clustering. We use our algorithm to cluster proteins by sequence similarity. This setting nicely fits our model because we can use a fast sequence database search program to query a sequence against an entire dataset. We conduct an empirical study that shows that even though we query a small fraction of the distances between the points, we produce clusterings that are close to a desired clustering given by manual classification.

Connected through hyperedge HE-1 (induced by source paper "scalable and robust community detection with randomized sketching"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "community detection via random and adaptive sampling"
    - "community detection thresholds and the weak ramanujan property"
    - "sparse subspace clustering algorithm theory and applications"
    - "clustering partially observed graphs via convex optimization"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "approximate correlation clustering using same cluster queries"):
  with members:
    - "approximate clustering with same cluster queries"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "clustering with same cluster queries"
    - "predicting signed edges with o n 1 o 1 log n queries"
    - "correlation clustering with noisy partial information"
    - "local algorithms for interactive clustering"
    - "clustering under perturbation resilience"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-3 (induced by source paper "clustering partially observed graphs via convex optimization"):
  with members:
    - "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"
    - "active clustering robust and efficient hierarchical clustering using adaptively selected similarities"
    - "low rank matrix recovery from errors and erasures"
    - "nuclear norm minimization for the planted clique and biclique problems"
    - "a simpler approach to matrix completion"

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)