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: "energy efficient adaptive video transmission exploiting rate predictions in wireless networks"
  Abstract: The unprecedented growth of mobile video traffic is adding significant pressure to the energy drain at both the network and the end user. Energy efficient video transmission techniques are thus imperative to cope with the challenge of satisfying user demand at sustainable costs. In this paper, we investigate how predicted user rates can be exploited for energy efficient video streaming with the popular HTTP-based Adaptive Streaming (AS) protocols (e.g. DASH). To this end, we develop an energy-efficient Predictive Green Streaming (PGS) optimization framework that leverages predictions of wireless data rates to achieve the following objectives 1) minimize the required transmission airtime without causing streaming interruptions, 2) minimize total downlink Base Station (BS) power consumption for cases where BSs can be switched off in deep sleep, and 3) enable a trade-off between AS quality and energy consumption. Our framework is first formulated as a Mixed Integer Linear Program (MILP) where decisions on multi-user rate allocation, video segment quality, and BS transmit power are jointly optimized. Then, to provide an online solution, we present a polynomial-time heuristic algorithm that decouples the PGS problem into multiple stages. We provide a performance analysis of the proposed methods by simulations, and numerical results demonstrate that the PGS framework yields significant energy savings.

Connected through hyperedge HE-1 (induced by source paper "power control for wireless vbr video streaming from optimization to reinforcement learning"):
  with members:
    - "power control and mode selection for vbr video streaming in d2d networks"
    - "quality driven resource allocation for full duplex delay constrained wireless video transmissions"
    - "online energy efficient packet scheduling for a common deadline with and without energy harvesting"
    - "universally near optimal online power control for energy harvesting nodes"
    - "optimal packet scheduling in an energy harvesting communication system"

Connected through hyperedge HE-2 (induced by source paper "energy efficient resource allocation for hybrid services with future channel gains"):
  with members:
    - "an lstm network for highway trajectory prediction"
    - "energy efficient resource allocation in machine to machine communications with multiple access and energy harvesting for iot"
    - "fundamental green tradeoffs progresses challenges and impacts on 5g networks"
    - "anticipatory radio resource management for mobile video streaming with linear programming"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"
    - "adaptive video streaming for wireless networks with multiple users and helpers"

Connected through hyperedge HE-3 (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-4 (induced by source paper "role of large scale channel information on predictive resource allocation"):
  with members:
    - "predictive green wireless access exploiting mobility and application information"

Connected through hyperedge HE-5 (induced by source paper "context aware small cell networks how social metrics improve wireless resource allocation"):
  with members:
    - "matching theory for priority based cell association in the downlink of wireless small cell networks"
    - "five disruptive technology directions for 5g"
    - "peer effects and stability in matching markets"

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)