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: "queueing analysis of unicast iptv with adaptive modulation and coding in wireless cellular networks"
  Abstract: Unicast Internet protocol television (IPTV) services that can support live TV, video-on-demand, video conferencing, and online gaming applications over broadband wireless cellular networks have been becoming popular for many years. However, video streaming services significantly impact the performance of wireless cellular networks because they are bandwidth hogs. To maintain the system performance, effective admission control and resource allocation mechanisms based on an accurate mathematical analysis are required. On the other hand, the quality of a wireless link usually changes with time because of user mobility or time-varying channel characteristics. To counteract such time-varying channels and improve the spectral efficiency, an adaptive modulation and coding (AMC) scheme is adopted in offering unicast IPTV services for mobile users. In this paper, closed-form solutions for the bandwidth usage, blocking rate, and dropping rate of unicast IPTV services over wireless cellular networks were derived based on the novel queueing model that considers AMC. Simulations were also conducted to validate the accuracy of analytical results. Numerical results demonstrate that the presented analytical results are accurate. Based on the accurate closed-form solutions, network providers can implement precise admission control and resource allocation for their networks to enhance the system performance.

Connected through hyperedge HE-1 (induced by source paper "queuing theory guided intelligent traffic scheduling through video analysis using dirichlet process mixture model"):
  with members:
    - "temporal unknown incremental clustering model for analysis of traffic surveillance videos"
    - "high speed tracking with kernelized correlation filters"
    - "the use of machine learning algorithms in recommender systems a systematic review"

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)