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 "detecting weak changes in dynamic events over networks"):
  Abstract: Large volume of networked streaming event data are becoming increasingly available in a wide variety of applications, such as social network analysis, Internet traffic monitoring and healthcare analytics. Streaming event data are discrete observation occurred in continuous time, and the precise time interval between two events carries a great deal of information about the dynamics of the underlying systems. How to promptly detect changes in these dynamic systems using these streaming event data? In this paper, we propose a novel change-point detection framework for multi-dimensional event data over networks. We cast the problem into sequential hypothesis test, and derive the likelihood ratios for point processes, which are computed efficiently via an EM-like algorithm that is parameter-free and can be computed in a distributed fashion. We derive a highly accurate theoretical characterization of the false-alarm-rate, and show that it can achieve weak signal detection by aggregating local statistics over time and networks. Finally, we demonstrate the good performance of our algorithm on numerical examples and real-world datasets from twitter and Memetracker.

Includes members:
  Member "multistage campaigning in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "meta learning with relational information for short sequences")
  Member "learning granger causality for hawkes processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "coevolve a joint point process model for information diffusion and network co evolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks")
  Member "shaping social activity by incentivizing users":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self attentive hawkes processes")
  Member "the bursty dynamics of the twitter information network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computational human dynamics")
  Member "distinguishing infections on different graph topologies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the cost of uncertainty in curing epidemics")
  Member "modeling events with cascades of poisson processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a multi channel neural graphical event model with negative evidence")
  Member "uncovering the temporal dynamics of diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep collaborative embedding for information cascade prediction")

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.LG(Machine Learning)