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 "top k densest subgraphs in sliding window graph streams"):
  Abstract: Given a large graph, the densest-subgraph problem asks to find a subgraph with maximum average degree. When considering the top-k version of this problem, a naive solution is to iteratively find the densest subgraph and remove it in each iteration. However, such a solution is impractical due to high processing cost. The problem is further complicated when dealing with dynamic graphs, since adding or removing an edge requires re-running the algorithm. In this paper, we study the top-k densest subgraph problem in the sliding-window model and propose an efficient fully-dynamic algorithm. The input of our algorithm consists of an edge stream, and the goal is to find the node-disjoint subgraphs that maximize the sum of their densities. In contrast to existing state-of-the-art solutions that require iterating over the entire graph upon any update, our algorithm profits from the observation that updates only affect a limited region of the graph. Therefore, the top-k densest subgraphs are maintained by only applying local updates. We provide a theoretical analysis of the proposed algorithm and show empirically that the algorithm often generates denser subgraphs than state-of-the-art competitors. Experiments show an improvement in efficiency of up to three to five orders of magnitude compared to state-of-the-art solutions.

Includes members:
  Member "a fast order based approach for core maintenance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of community search over big graphs")
  Member "densest subgraph in dynamic graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "density friendly graph decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "computing the k densest subgraphs of a graph")
  Member "when two choices are not enough balancing at scale in distributed stream processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "swarm adaptive load balancing in distributed streaming systems for big spatial data")
  Member "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "fast exact shortest path distance queries on large networks by pruned landmark labeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relaxing relationship queries on graph data")
  Member "densest subgraph in streaming and mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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)