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 "intrusion detection using continuous time bayesian networks"):
  Abstract: Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset.

Includes members:
  Member "rao blackwellised particle filtering for dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "poserbpf a rao blackwellized particle filter for 6d object pose tracking")
  Member "continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series")
  Member "learning continuous time social network dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "listen to your face inferring facial action units from audio channel")
  Member "learning continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "listen to your face inferring facial action units from audio channel")
  Member "expectation maximization and complex duration distributions for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a hidden absorbing semi markov model for informatively censored temporal data learning and inference")
  Member "mean field variational approximation for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a variational perturbative approach to planning in graph based markov decision processes")
  Member "expectation propagation for continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ctbnctoolkit continuous time bayesian network classifier toolkit")
  Member "ct nor representing and reasoning about events in continuous time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a multi channel neural graphical event model with negative evidence")

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.AI(Artificial Intelligence)