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: "anticipatory radio resource management for mobile video streaming with linear programming"
  Abstract: In anticipatory networking, channel prediction is used to improve communication performance. This paper describes a new approach for allocating resources to video streaming traffic while accounting for quality of service. The proposed method is based on integrating a model of the user's local play-out buffer into the radio access network. The linearity of this model allows to formulate a Linear Programming problem that optimizes the trade-off between the allocated resources and the stalling time of the media stream. Our simulation results demonstrate the full power of anticipatory optimization in a simple, yet representative, scenario. Compared to instantaneous adaptation, our anticipatory solution shows impressive gains in spectral efficiency and stalling duration at feasible computation time while being robust against prediction errors.

Connected through hyperedge HE-1 (induced by source paper "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks"):
  with members:
    - "traffic profiling for mobile video streaming"
    - "a comparative case study of http adaptive streaming algorithms in mobile networks"

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"
    - "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"
    - "energy efficient adaptive video transmission exploiting rate predictions in wireless networks"

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"
    - "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"
    - "anticipatory mobile computing a survey of the state of the art and research 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)