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: "anomaly detection in log data using graph databases and machine learning to defend advanced persistent threats"
  Abstract: Advanced Persistent Threats (APTs) are a main impendence in cyber security of computer networks. In 2015, a successful breach remains undetected 146 days on average, reported by [Fi16].With our work we demonstrate a feasible and fast way to analyse real world log data to detect breaches or breach attempts. By adapting well-known kill chain mechanisms and a combine of a time series database and an abstracted graph approach, it is possible to create flexible attack profiles. Using this approach, it can be demonstrated that the graph analysis successfully detects simulated attacks by analysing the log data of a simulated computer network. Considering another source for log data, the framework is capable to deliver sufficient performance for analysing real-world data in short time. By using the computing power of the graph database it is possible to identify the attacker and furthermore it is feasible to detect other affected system components. We believe to significantly reduce the detection time of breaches with this approach and react fast to new attack vectors.

Connected through hyperedge HE-1 (induced by source paper "next stop noops enabling cross system diagnostics through graph based composition of logs and metrics"):
  with members:
    - "anomaly detection on graph time series"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "graph based anomaly detection and description a survey"
    - "distributed representations of words and phrases and their compositionality"

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.DB(Databases)