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: "glop enabling massively parallel incident response through gpu log processing"
  Abstract: Large industrial systems that combine services and applications, have become targets for cyber criminals and are challenging from the security, monitoring and auditing perspectives. Security log analysis is a key step for uncovering anomalies, detecting intrusion, and enabling incident response. The constant increase of link speeds, threats and users, produce large volumes of log data and become increasingly difficult to analyse on a Central Processing Unit (CPU). This paper presents a massively parallel Graphics Processing Unit (GPU) Log Processing (GLoP) library and can also be used for Deep Packet Inspection (DPI), using a prefix matching technique, harvesting the full power of off-the-shelf technologies. GLoP implements two different algorithm using different GPU memory and is compared against CPU counterpart implementations. The library can be used for processing nodes with single or multiple GPUs as well as GPU cloud farms. The results show throughput of 20 Gbps and demonstrate that modern GPUs can be utilised to increase the operational speed of large scale log processing scenarios, saving precious time before and after an intrusion has occurred.

Connected through hyperedge HE-1 (induced by source paper "from cyber security deception to manipulation and gratification through gamification"):
  with members:
    - "a taxonomy and survey of intrusion detection system design techniques network threats and datasets"
    - "a survey on honeypot software and data analysis"

Connected through hyperedge HE-2 (induced by source paper "a taxonomy and survey of intrusion detection system design techniques network threats and datasets"):
  with members:
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "machine learning approach for detection of nontor traffic"
    - "threat analysis of iot networks using artificial neural network intrusion detection system"
    - "a new clustering approach for anomaly intrusion detection"
    - "novel intrusion detection using probabilistic neural network and adaptive boosting"

Connected through hyperedge HE-3 (induced by source paper "a taxonomy of malicious traffic for intrusion detection systems"):
  with members:
    - "sequence covering for efficient host based intrusion detection"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"

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.DC(Distributed, Parallel, and Cluster Computing)