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 "sketch based influence maximization and computation scaling up with guarantees"):
  Abstract: Propagation of contagion through networks is a fundamental process. It is used to model the spread of information, influence, or a viral infection. Diffusion patterns can be specified by a probabilistic model, such as Independent Cascade (IC), or captured by a set of representative traces.   Basic computational problems in the study of diffusion are influence queries (determining the potency of a specified seed set of nodes) and Influence Maximization (identifying the most influential seed set of a given size). Answering each influence query involves many edge traversals, and does not scale when there are many queries on very large graphs. The gold standard for Influence Maximization is the greedy algorithm, which iteratively adds to the seed set a node maximizing the marginal gain in influence. Greedy has a guaranteed approximation ratio of at least (1-1/e) and actually produces a sequence of nodes, with each prefix having approximation guarantee with respect to the same-size optimum. Since Greedy does not scale well beyond a few million edges, for larger inputs one must currently use either heuristics or alternative algorithms designed for a pre-specified small seed set size.   We develop a novel sketch-based design for influence computation. Our greedy Sketch-based Influence Maximization (SKIM) algorithm scales to graphs with billions of edges, with one to two orders of magnitude speedup over the best greedy methods. It still has a guaranteed approximation ratio, and in practice its quality nearly matches that of exact greedy. We also present influence oracles, which use linear-time preprocessing to generate a small sketch for each node, allowing the influence of any seed set to be quickly answered from the sketches of its nodes.

Includes members:
  Member "influence maximization near optimal time complexity meets practical efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "time constrained adaptive influence maximization")
  Member "all distances sketches revisited hip estimators for massive graphs analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling sketches for concave sublinear functions of frequencies")
  Member "trace complexity of network inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning mixtures of graphs from epidemic cascades")
  Member "scalable influence estimation in continuous time diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate k cover in hypergraphs efficient algorithms and applications")
  Member "maximizing social influence in nearly optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time constrained adaptive influence maximization")
  Member "time critical influence maximization in social networks with time delayed diffusion process":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "time constrained adaptive influence maximization")
  Member "uncovering the temporal dynamics of diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "irie scalable and robust influence maximization in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "limitations of greed influence maximization in undirected networks re visited")

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)