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: "efficient access of mobile flows to heterogeneous networks under flash crowds"
  Abstract: Future wireless networks need to offer orders of magnitude more capacity to address the predicted growth in mobile traffic demand. Operators to enhance the capacity of cellular networks are increasingly using WiFi to offload traffic from their core networks. This paper deals with the efficient and flexible management of a heterogeneous networking environment offering wireless access to multimode terminals. This wireless access is evaluated under disruptive usage scenarios, such as flash crowds, which can mean unwanted severe congestion on a specific operator network whilst the remaining available capacity from other access technologies is not being used. To address these issues, we propose a scalable network assisted distributed solution that is administered by centralized policies, and an embedded reputation system, by which initially selfish operators are encouraged to cooperate under the threat of churn. Our solution after detecting a congested technology, including within its wired backhaul, automatically offloads and balances the flows amongst the access resources from all the existing technologies, following some quality metrics. Our results show that the smart integration of access networks can yield an additional wireless quality for mobile flows up to thirty eight percent beyond that feasible from the best effort standalone operation of each wireless access technology. It is also evidenced that backhaul constraints are conveniently reflected on the way the flow access to wireless media is granted. Finally, we have analyzed the sensitivity of the handover decision algorithm running in each terminal agent to consecutive flash crowds, as well as its centralized feature that controls the connection quality offered by a heterogeneous access infrastructure owned by distinct operators.

Connected through hyperedge HE-1 (induced by source paper "game theory for multi access edge computing survey use cases and future trends"):
  with members:
    - "non cooperative game theory based rate adaptation for dynamic video streaming over http"
    - "fog computing a taxonomy survey and future directions"
    - "optimal pricing for a peer to peer sharing platform under network externalities"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "a survey on mobile edge computing the communication perspective"
    - "sustainable incentives for mobile crowdsensing"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "mobile edge computing a survey on architecture and computation offloading"

Connected through hyperedge HE-2 (induced by source paper "survey of game theory and future trends with application to emerging wireless data communication networks"):
  with members:
    - "non cooperative game theory based rate adaptation for dynamic video streaming over http"
    - "fog computing a taxonomy survey and future directions"
    - "a survey on mobile edge computing the communication perspective"
    - "sustainable incentives for mobile crowdsensing"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "game theory models for communication between agents a review"
    - "game theoretic multi agent control and network cost allocation under communication constraints"
    - "a survey on applications of model free strategy learning in cognitive wireless 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)