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 "a survey on big data for network traffic monitoring and analysis"):
  Abstract: Network Traffic Monitoring and Analysis (NTMA) represents a key component for network management, especially to guarantee the correct operation of large-scale networks such as the Internet. As the complexity of Internet services and the volume of traffic continue to increase, it becomes difficult to design scalable NTMA applications. Applications such as traffic classification and policing require real-time and scalable approaches. Anomaly detection and security mechanisms require to quickly identify and react to unpredictable events while processing millions of heterogeneous events. At last, the system has to collect, store, and process massive sets of historical data for post-mortem analysis. Those are precisely the challenges faced by general  big data  approaches: Volume, Velocity, Variety, and Veracity. This survey brings together NTMA and big data. We catalog previous work on NTMA that adopt big data approaches to understand to what extent the potential of big data is being explored in NTMA. This survey mainly focuses on approaches and technologies to manage the big NTMA data, additionally briefly discussing big data analytics (e.g., machine learning) for the sake of NTMA. Finally, we provide guidelines for future work, discussing lessons learned, and research directions.

Includes members:
  Member "data analytics service composition and deployment on edge devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey")
  Member "net2vec deep learning for the network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised machine learning for networking techniques applications and research challenges")
  Member "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast online low rank tensor subspace tracking by cp decomposition using recursive least squares from incomplete observations")
  Member "in a world that counts clustering and detecting fake social engagement at scale":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook")
  Member "a new approach to dimensionality reduction for anomaly detection in data traffic":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey of network intrusion detection methods from the perspective of the knowledge discovery in databases process")
  Member "efficacy of live ddos detection with hadoop":
    also connected through other hyperedges:
      (none)
  Member "the family of mapreduce and large scale data processing systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a taxonomy on big data survey")
  Member "building high level features using large scale unsupervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")

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.DC(Distributed, Parallel, and Cluster Computing)