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: "patient centric cellular networks optimization using big data analytics"
  Abstract: Big data analytics is one of the state-of-the-art tools to optimize networks and transform them from merely being a blind tube that conveys data, into a cognitive, conscious, and self-optimizing entity that can intelligently adapt according to the needs of its users. This, in fact, can be regarded as one of the highest forthcoming priorities of future networks. In this paper, we propose a system for Out-Patient (OP) centric Long Term Evolution-Advanced (LTE-A) network optimization. Big data harvested from the OPs' medical records, along with current readings from their body sensors are processed and analyzed to predict the likelihood of a life-threatening medical condition, for instance, an imminent stroke. This prediction is used to ensure that the OP is assigned an optimal LTE-A Physical Resource Blocks (PRBs) to transmit their critical data to their healthcare provider with minimal delay. To the best of our knowledge, this is the first time big data analytics are utilized to optimize a cellular network in an OP-conscious manner. The PRBs assignment is optimized using Mixed Integer Linear Programming (MILP) and a real-time heuristic. Two approaches are proposed, the Weighted Sum Rate Maximization (WSRMax) approach and the Proportional Fairness (PF) approach. The approaches increased the OPs' average SINR by 26.6% and 40.5%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, however, the PF approach reported higher SINRs for the OPs, better fairness and a lower margin of error.

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"
    - "big data analytics for wireless and wired network design a survey"
    - "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 distributed processing for iot"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "service embedding in iot networks"
    - "energy efficient iot virtualization framework with peer to peer networking and processing"
    - "optimized energy aware 5g network function virtualization"
    - "allocation of heterogeneous resources of an iot device to flexible services"

Connected through hyperedge HE-3 (induced by source paper "energy efficient virtual machines placement over cloud fog network architecture"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "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-4 (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-5 (induced by source paper "energy efficient routing and network coding in core networks"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "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-6 (induced by source paper "resilient service embedding in iot networks"):
  with members:
    - "service embedding in iot networks"
    - "big data analytics for wireless and wired network design a survey"
    - "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-7 (induced by source paper "energy efficient and resilient infrastructure for fog computing health monitoring applications"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "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-8 (induced by source paper "virtual machines embedding for cloud pon awgr and server based data centres"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "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"

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)