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: "primal profit maximization avatar placement for mobile edge computing"
  Abstract: We propose a cloudlet network architecture to bring the computing resources from the centralized cloud to the edge. Thus, each User Equipment (UE) can communicate with its Avatar, a software clone located in a cloudlet, with lower end-to-end (E2E) delay. However, UEs are moving over time, and so the low E2E delay may not be maintained if UEs' Avatars stay in their original cloudlets. Thus, live Avatar migration (i.e., migrating a UE's Avatar to a suitable cloudlet based on the UE's location) is enabled to maintain low E2E delay between each UE and its Avatar. On the other hand, the migration itself incurs extra overheads in terms of resources of the Avatar, which compromise the performance of applications running in the Avatar. By considering the gain (i.e., the E2E delay reduction) and the cost (i.e., the migration overheads) of the live Avatar migration, we propose a PRofIt Maximization Avatar pLacement (PRIMAL) strategy for the cloudlet network in order to optimize the tradeoff between the migration gain and the migration cost by selectively migrating the Avatars to their optimal locations. Simulation results demonstrate that as compared to the other two strategies (i.e., Follow Me Avatar and Static), PRIMAL maximizes the profit in terms of maintaining the low average E2E delay between UEs and their Avatars and minimizing the migration cost simultaneously.

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"
    - "data analytics service composition and deployment on edge devices"
    - "live service migration in mobile edge clouds"
    - "on reducing iot service delay via fog offloading"

Connected through hyperedge HE-2 (induced by source paper "hierarchical capacity provisioning for fog computing"):
  with members:
    - "profit maximization for geographically dispersed green data centers"
    - "toward hierarchical mobile edge computing an auction based profit maximization approach"
    - "green cloudlet network a distributed green mobile cloud network"
    - "an auction mechanism for resource allocation in mobile cloud computing systems"

Connected through hyperedge HE-3 (induced by source paper "mobile edge computing a survey on architecture and computation offloading"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "resource management in cloud networking using economic analysis and pricing models a survey"
    - "online placement of multi component applications in edge computing environments"
    - "dynamic service placement for mobile micro clouds with predicted future costs"
    - "delay optimal computation task scheduling for mobile edge computing systems"
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "multiuser resource allocation for mobile edge computation offloading"

Connected through hyperedge HE-4 (induced by source paper "mobile edge computing empowers internet of things"):
  with members:
    - "green cloudlet network a distributed green mobile cloud network"
    - "access control in internet of things a survey"
    - "green energy aware avatar migration strategy in green cloudlet networks"
    - "cognitive internet of things a new paradigm beyond connection"

Connected through hyperedge HE-5 (induced by source paper "toward hierarchical mobile edge computing an auction based profit maximization approach"):
  with members:
    - "profit maximization for geographically dispersed green data centers"
    - "inter layer per mobile optimization of cloud mobile computing a message passing approach"
    - "an auction mechanism for resource allocation in mobile cloud computing systems"

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)