human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "repad real time proactive anomaly detection for time series"):
  with members:
    - "greenhouse a zero positive machine learning system for time series anomaly detection"
    - "automatic anomaly detection in the cloud via statistical learning"
    - "hybrid job driven scheduling for virtual mapreduce clusters"
    - "evaluating real time anomaly detection algorithms the numenta anomaly benchmark"

Connected through hyperedge HE-2 (induced by source paper "dynamic graph embedding via lstm history tracking"):
  with members:
    - "dynamicgem a library for dynamic graph embedding methods"
    - "dyngem deep embedding method for dynamic graphs"
    - "learning structural node embeddings via diffusion wavelets"
    - "dynnode2vec scalable dynamic network embedding"
    - "representation learning over dynamic graphs"
    - "sentence state lstm for text representation"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "process monitoring on sequences of system call count vectors"):
  with members:
    - "lstm based system call language modeling and robust ensemble method for designing host based intrusion detection systems"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "a planning approach to monitoring behavior of computer programs"):
  with members:
    - "lstm based system call language modeling and robust ensemble method for designing host based intrusion detection systems"

Connected through hyperedge HE-5 (induced by source paper "burstiness of intrusion detection process empirical evidence and a modeling approach"):
  with members:
    - "data reduction in intrusion alert correlation"

Connected through hyperedge HE-6 (induced by source paper "factored filtering of continuous time systems"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "continuous time bayesian networks"
    - "learning continuous time social network dynamics"
    - "expectation maximization and complex duration distributions for continuous time bayesian networks"
    - "mean field variational approximation for continuous time bayesian networks"

Question: Please tell me which class the center node 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)