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: "lbp robust rate adaptation algorithm for svc video streaming"
  Abstract: Video streaming today accounts for up to 55% of mobile traffic. In this paper, we explore streaming videos encoded using scalable video coding (SVC) scheme over highly variable bandwidth conditions, such as cellular networks. SVC’s unique encoding scheme allows the quality of a video chunk to change incrementally, making it more flexible and adaptive to challenging network conditions compared to other encoding schemes. Our contribution is threefold. First, we formulate the quality decisions of video chunks constrained by the available bandwidth, the playback buffer, and the chunk deadlines as an optimization problem. The objective is to optimize a novel quality-of-experience metric that models a combination of the three objectives of minimizing the stall/skip duration of the video, maximizing the playback quality of every chunk, and minimizing the number of quality switches. Second, we develop layered bin packing (LBP) adaptation algorithm, a novel algorithm that solves the proposed optimization problem. Moreover, we show that LBP achieves the optimal solution of the proposed optimization problem with linear complexity in the number of video chunks. Third, we propose an online algorithm (online LBP) where several challenges are addressed, including handling bandwidth prediction errors and short prediction duration. Extensive simulations with real bandwidth traces of public datasets reveal the robustness of our scheme and demonstrate its significant performance improvement as compared with the state-of-the-art SVC streaming algorithms. The proposed algorithm is also implemented on a TCP/IP emulation test bed with real LTE bandwidth traces, and the emulation confirms the simulation results and validates that the algorithm can be implemented and deployed on today’s mobile devices.

Connected through hyperedge HE-1 (induced by source paper "enhancing video streaming in vehicular networks via resource slicing"):
  with members:
    - "fastscan robust low complexity rate adaptation algorithm for video streaming over http"
    - "towards low latency and ultra reliable vehicle to vehicle communication"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "system level performance evaluation of lte v2x network"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "generalization of lru cache replacement policy with applications to video streaming"):
  with members:
    - "fastscan robust low complexity rate adaptation algorithm for video streaming over http"
    - "optimizing tcp loss recovery performance over mobile data networks"
    - "sprout a functional caching approach to minimize service latency in erasure coded storage"
    - "impact of traffic mix on caching performance in a content centric network"
    - "performance evaluation of the random replacement policy for networks of caches"
    - "a versatile and accurate approximation for lru cache performance"

Connected through hyperedge HE-3 (induced by source paper "groupcast preference aware cooperative video streaming with scalable video coding"):
  with members:
    - "fastscan robust low complexity rate adaptation algorithm for video streaming over http"
    - "optimized preference aware multi path video streaming with scalable video coding"

Connected through hyperedge HE-4 (induced by source paper "fastscan robust low complexity rate adaptation algorithm for video streaming over http"):
  with members:
    - "bola near optimal bitrate adaptation for online videos"
    - "a control theoretic approach to adaptive video streaming in dense wireless networks"

Connected through hyperedge HE-5 (induced by source paper "video streaming in distributed erasure coded storage systems stall duration analysis"):
  with members:
    - "optimized video streaming over cloud a stall quality trade off"
    - "joint latency and cost optimization for erasure coded data center storage"
    - "when queueing meets coding optimal latency data retrieving scheme in storage clouds"
    - "xoring elephants novel erasure codes for big data"
    - "on the delay storage trade off in content download from coded distributed storage systems"
    - "the mds queue analysing the latency performance of erasure codes"

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)