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: "predictive green wireless access exploiting mobility and application information"
  Abstract: The ever increasing mobile data traffic and dense deployment of wireless networks have made energy efficient radio access imperative. As networks are designed to satisfy peak user demands, radio access energy can be reduced in a number of ways at times of lower demand. This includes putting base stations (BSs) to intermittent short sleep modes during low load, as well as adaptively powering down select BSs completely where demand is low for prolonged time periods. In order to fully exploit such energy conserving mechanisms, networks should be aware of the user temporal and spatial traffic demands. To this end, this article investigates the potential of utilizing predictions of user location and application information as a means to energy saving. We discuss the development of a predictive green wireless access (PreGWA) framework and identify its key functional entities and their interaction. To demonstrate the potential energy savings we then provide a case study on stored video streaming and illustrate how exploiting predictions can minimize BS resource consumption within a single cell, and across a network of cells. Finally, to emphasize the practical potential of PreGWA, we present a distributed heuristic that reduces resource consumption significantly without requiring considerable information or signaling overhead.

Connected through hyperedge HE-1 (induced by source paper "energy efficient predictive resource allocation for vod and real time services"):
  with members:
    - "fundamental green tradeoffs progresses challenges and impacts on 5g networks"
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "an overview of sustainable green 5g networks"
    - "adaptive video streaming for wireless networks with multiple users and helpers"

Connected through hyperedge HE-2 (induced by source paper "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"):
  with members:
    - "context aware scheduling of joint millimeter wave and microwave resources for dual mode base stations"
    - "anticipatory radio resource management for mobile video streaming with linear programming"
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "on characterization of feasible interference regions in cognitive radio networks"
    - "spatial wireless channel prediction under location uncertainty"
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"
    - "5g cellular key enabling technologies and research challenges"
    - "context aware small cell networks how social metrics improve wireless resource allocation"

Connected through hyperedge HE-3 (induced by source paper "anticipatory networking in future generation mobile networks a survey"):
  with members:
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "spatial wireless channel prediction under location uncertainty"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "rate adaptation and admission control for video transmission with subjective quality constraints"
    - "predicting a user s next cell with supervised learning based on channel states"
    - "a comparative study of collaborative filtering algorithms"
    - "femtocaching wireless video content delivery through distributed caching helpers"
    - "mobility prediction in wireless ad hoc networks using neural networks"

Connected through hyperedge HE-4 (induced by source paper "role of large scale channel information on predictive resource allocation"):
  with members:
    - "energy efficient adaptive video transmission exploiting rate predictions in wireless networks"

Connected through hyperedge HE-5 (induced by source paper "quantifying potential energy efficiency gain in green cellular wireless networks"):
  with members:
    - "exploiting network cooperation in green wireless communication"
    - "green cellular networks a survey some research issues and challenges"
    - "beamsteering on mobile devices network capacity and client efficiency"
    - "a survey of green networking research"
    - "femtocell networks a survey"

Connected through hyperedge HE-6 (induced by source paper "energy efficient adaptive video transmission exploiting rate predictions in wireless networks"):
  with members:
    - "nova qoe driven optimization of dash based video delivery in networks"
    - "predicting a user s next cell with supervised learning based on channel states"
    - "green cellular networks a survey some research issues and challenges"

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)