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: "a control theoretic approach to adaptive video streaming in dense wireless networks"
  Abstract: Recently, the way people consume video content has been undergoing a dramatic change. Plain TV sets, that have been the center of home entertainment for a long time, are losing ground to hybrid TVs, PCs, game consoles, and, more recently, mobile devices such as tablets and smartphones. The new predominant paradigm is: watch what I want, when I want, and where I want. The challenges of this shift are manifold. On the one hand, broadcast technologies such as DVB-T/C/S need to be extended or replaced by mechanisms supporting asynchronous viewing, such as IPTV and video streaming over best-effort networks, while remaining scalable to millions of users. On the other hand, the dramatic increase of wireless data traffic begins to stretch the capabilities of the existing wireless infrastructure to its limits. Finally, there is a challenge to video streaming technologies to cope with a high heterogeneity of end-user devices and dynamically changing network conditions, in particular in wireless and mobile networks. In the present work, our goal is to design an efficient system that supports a high number of unicast streaming sessions in a dense wireless access network. We address this goal by jointly considering the two problems of wireless transmission scheduling and video quality adaptation, using techniques inspired by the robustness and simplicity of proportional-integral-derivative (PID) controllers. We show that the control-theoretic approach allows to efficiently utilize available wireless resources, providing high quality of experience (QoE) to a large number of users.

Connected through hyperedge HE-1 (induced by source paper "understanding video streaming algorithms in the wild"):
  with members:
    - "learning in situ a randomized experiment in video streaming"
    - "performance characterization of a commercial video streaming service"
    - "bola near optimal bitrate adaptation for online videos"
    - "which adaptation logic an objective and subjective performance evaluation of http based adaptive media streaming systems"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-2 (induced by source paper "lbp robust rate adaptation algorithm for svc video streaming"):
  with members:
    - "groupcast preference aware cooperative video streaming with scalable video coding"
    - "optimized preference aware multi path video streaming with scalable video coding"
    - "qoe based low delay live streaming using throughput predictions"
    - "msplayer multi source and multi path leveraged youtuber"

Connected through hyperedge HE-3 (induced by source paper "fastscan robust low complexity rate adaptation algorithm for video streaming over http"):
  with members:
    - "lbp robust rate adaptation algorithm for svc video streaming"
    - "bola near optimal bitrate adaptation for online videos"

Connected through hyperedge HE-4 (induced by source paper "a survey on qoe oriented wireless resources scheduling"):
  with members:
    - "an overview on resource allocation techniques for multi user mimo systems"
    - "wiflix adaptive video streaming in massive mu mimo wireless networks"
    - "nova qoe driven optimization of dash based video delivery in networks"
    - "rate adaptation and admission control for video transmission with subjective quality constraints"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "qoe based low delay live streaming using throughput predictions"):
  with members:
    - "adaptive video streaming for wireless networks with multiple users and helpers"
    - "streaming video over http with consistent quality"
    - "probe and adapt rate adaptation for http video streaming at scale"

Connected through hyperedge HE-7 (induced by source paper "wiflix adaptive video streaming in massive mu mimo wireless networks"):
  with members:
    - "performance analysis of massive mimo for cell boundary users"
    - "adaptive video streaming for wireless networks with multiple users and helpers"
    - "achieving large multiplexing gain in distributed antenna systems via cooperation with pcell technology"
    - "optimal user cell association for massive mimo wireless networks"
    - "what will 5g be"
    - "nova qoe driven optimization of dash based video delivery in networks"
    - "loss visibility optimized real time video transmission over mimo systems"
    - "joint spatial division and multiplexing for mm wave channels"

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)