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: "joint service placement and request routing in multi cell mobile edge computing networks"
  Abstract: The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.

Connected through hyperedge HE-1 (induced by source paper "joint optimization of service caching placement and computation offloading in mobile edge computing system"):
  with members:
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "spatio temporal edge service placement a bandit learning approach"
    - "deep reinforcement learning for online computation offloading in wireless powered mobile edge computing networks"
    - "price based distributed offloading for mobile edge computing with computation capacity constraints"
    - "a survey on mobile edge computing the communication perspective"
    - "energy efficient resource allocation for cache assisted mobile edge computing"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"
    - "live prefetching for mobile computation offloading"

Connected through hyperedge HE-2 (induced by source paper "okpi all kpi network slicing through efficient resource allocation"):
  with members:
    - "a utility driven multi queue admission control solution for network slicing"
    - "getting the most out of your vnfs flexible assignment of service priorities in 5g"
    - "vnf placement and resource allocation for the support of vertical services in 5g networks"
    - "joint service caching and task offloading for mobile edge computing in dense networks"
    - "jasper joint optimization of scaling placement and routing of virtual network services"
    - "z torch an automated nfv orchestration and monitoring solution"
    - "provably efficient algorithms for joint placement and allocation of virtual network functions"
    - "approximate and incremental network function placement"

Connected through hyperedge HE-3 (induced by source paper "dynamic spectrum sharing for load balancing in multi cell mobile edge computing"):
  with members:
    - "mobile edge computing enabled heterogeneous networks"
    - "multiuser computation offloading and downloading for edge computing with virtualization"
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"
    - "joint optimization of radio and computational resources for multicell mobile edge computing"
    - "efficient multi user computation offloading for mobile edge cloud computing"
    - "user association for load balancing in heterogeneous cellular networks"

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)