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: "data analytics service composition and deployment on edge devices"
  Abstract: Data analytics on edge devices has gained rapid growth in research, industry, and different aspects of our daily life. This topic still faces many challenges such as limited computation resource on edge devices. In this paper, we further identify two main challenges: the composition and deployment of data analytics services on edge devices. We present the Zoo system to address these two challenge: on one hand, it provides simple and concise domain-specific language to enable easy and and type-safe composition of different data analytics services; on the other, it utilises multiple deployment backends, including Docker container, JavaScript, and MirageOS, to accommodate the heterogeneous edge deployment environment. We show the expressiveness of Zoo with a use case, and thoroughly compare the performance of different deployment backends in evaluation.

Connected through hyperedge HE-1 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "are fpgas suitable for edge computing"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "fog computing a taxonomy survey and future directions"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"
    - "towards mission critical control at the edge and over 5g"

Connected through hyperedge HE-2 (induced by source paper "a survey on big data for network traffic monitoring and analysis"):
  with members:
    - "net2vec deep learning for the network"
    - "network volume anomaly detection and identification in large scale networks based on online time structured traffic tensor tracking"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a new approach to dimensionality reduction for anomaly detection in data traffic"
    - "efficacy of live ddos detection with hadoop"
    - "the family of mapreduce and large scale data processing systems"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-3 (induced by source paper "distributing intelligence to the edge and beyond"):
  with members:
    - "a berkeley view of systems challenges for ai"
    - "practical black box attacks against machine learning"
    - "stealing machine learning models via prediction apis"
    - "communication efficient learning of deep networks from decentralized data"
    - "tensorflow a system for large scale machine learning"
    - "going deeper with convolutions"

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.SE(Software Engineering)