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 "mining maximal span cores from temporal networks"):
  Abstract: When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). We tackle this task by introducing a notion of temporal core decomposition where each core is associated with its span: we call such cores span-cores. As the total number of time intervals is quadratic in the size of the temporal domain T under analysis, the total number of span-cores is quadratic in $|T|$ as well. Our first contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores, i.e., span-cores that are not dominated by any other span-core by both the coreness property and the span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly compute the maximal ones without computing all span-cores. Experimentation on several real-world temporal networks confirms the efficiency and scalability of our methods. Applications on temporal networks, gathered by a proximity-sensing infrastructure recording face-to-face interactions in schools, highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics and detecting/correcting anomalies in the data.

Includes members:
  Member "best friends forever bff finding lasting dense subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries")
  Member "enumerating maximal cliques in temporal graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "computing maximal cliques in link streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "social resilience in online communities the autopsy of friendster":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "prediction of emerging technologies based on analysis of the us patent citation network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "dense subgraph maintenance under streaming edge weight updates for real time story identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mining density contrast subgraphs")
  Member "listing all maximal cliques in sparse graphs in near optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "teaser fast and certifiable point cloud registration")

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.SI(Social and Information Networks)