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: "big data analytics for wireless and wired network design a survey"
  Abstract: Abstract   Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions.  In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the networks’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle employing big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.

Connected through hyperedge HE-1 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks"):
  with members:
    - "using machine learning and big data analytics to prioritize outpatients in hetnets"
    - "service embedding in iot networks"
    - "patient centric cellular networks optimization using big data analytics"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "optimized energy aware 5g network function virtualization"
    - "stacking classifiers for anti spam filtering of e mail"

Connected through hyperedge HE-2 (induced by source paper "energy efficient virtual machines placement over cloud fog network architecture"):
  with members:
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "optimized energy aware 5g network function virtualization"

Connected through hyperedge HE-3 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks"):
  with members:
    - "fog assisted caching employing solar renewable energy for delivering video on demand service"
    - "energy efficient and resilient infrastructure for fog computing health monitoring applications"
    - "virtual machines embedding for cloud pon awgr and server based data centres"
    - "a survey on geographically distributed big data processing using mapreduce"
    - "disaggregation for improved efficiency in fog computing era"
    - "experimental evaluation of server centric passive optical network based data centre architecture"
    - "energy efficient distributed processing in vehicular cloud architecture"
    - "energy efficient software matching in vehicular fog"

Connected through hyperedge HE-4 (induced by source paper "energy efficient routing and network coding in core networks"):
  with members:
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "optimized energy aware 5g network function virtualization"
    - "optimization based linear network coding for general connections of continuous flows"
    - "overlay protection against link failures using network coding"
    - "network protection codes against link failures using network coding"
    - "genetic representations for evolutionary minimization of network coding resources"

Connected through hyperedge HE-5 (induced by source paper "resilient service embedding in iot networks"):
  with members:
    - "service embedding in iot networks"
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "energy efficiency of server centric pon data center architecture for fog computing"
    - "optimized energy aware 5g network function virtualization"

Connected through hyperedge HE-6 (induced by source paper "energy efficient and resilient infrastructure for fog computing health monitoring applications"):
  with members:
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "energy efficiency of fog computing health monitoring applications"
    - "optimized energy aware 5g network function virtualization"
    - "server placement with shared backups for disaster resilient clouds"

Connected through hyperedge HE-7 (induced by source paper "virtual machines embedding for cloud pon awgr and server based data centres"):
  with members:
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "impact of link failures on the performance of mapreduce in data center networks"
    - "energy efficiency of server centric pon data center architecture for fog computing"
    - "optimized energy aware 5g network function virtualization"

Connected through hyperedge HE-8 (induced by source paper "experimental evaluation of server centric passive optical network based data centre architecture"):
  with members:
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "experimental evaluation of passive optical network based data centre architecture"
    - "energy efficiency of server centric pon data center architecture for fog computing"
    - "optimized energy aware 5g network function virtualization"

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.NI(Networking and Internet Architecture)