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 "robust clustering oracle and local reconstructor of cluster structure of graphs"):
  Abstract: Due to the massive size of modern network data, local algorithms that run in sublinear time for analyzing the cluster structure of the graph are receiving growing interest. Two typical examples are local graph clustering algorithms that find a cluster from a seed node with running time proportional to the size of the output set, and clusterability testing algorithms that decide if a graph can be partitioned into a few clusters in the framework of property testing. #R##N#In this work, we develop sublinear time algorithms for analyzing the cluster structure of graphs with noisy partial information. By using conductance based definitions for measuring the quality of clusters and the cluster structure, we formalize a definition of noisy clusterable graphs with bounded maximum degree. The algorithm is given query access to the adjacency list to such a graph. We then formalize the notion of robust clustering oracle for a noisy clusterable graph, and give an algorithm that builds such an oracle in sublinear time, which can be further used to support typical queries (e.g., IsOutlier($s$), SameCluster($s,t$)) regarding the cluster structure of the graph in sublinear time. All the answers are consistent with a partition of $G$ in which all but a small fraction of vertices belong to some good cluster. We also give a local reconstructor for a noisy clusterable graph that provides query access to a reconstructed graph that is guaranteed to be clusterable in sublinear time. All the query answers are consistent with a clusterable graph which is guaranteed to be close to the original graph.

Includes members:
  Member "testing graph clusterability algorithms and lower bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph clustering via qubo and digital annealing")
  Member "approximate correlation clustering using same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correlation clustering with adaptive similarity queries")
  Member "approximate clustering with same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "same cluster querying for overlapping clusters")
  Member "clustering with same cluster queries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainable k means and k medians clustering")
  Member "learning communities in the presence of errors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "planted models for k way edge and vertex expansion")
  Member "testing cluster structure of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "walking randomly massively and efficiently")
  Member "how robust are reconstruction thresholds for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improper learning for non stochastic control")
  Member "correlation clustering with noisy partial information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards learning sparsely used dictionaries with arbitrary supports")

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)