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: "collaborative downloading in vanet using network coding"
  Abstract: Data downloading on the fly is the base of commercial data services in vehicular networks, such as office-on-wheels and entertainment-on-wheels. Due to the sparse special distribution of roadside Base Stations (BS) along the road, downloading through Roadside-to-Vehicle (R2V) connections is intermittent. When multiple vehicles with geographical proximity have a common interest in certain objects to download, they can collaborate to significantly reduce their overall download time. In this paper, we investigate the application of Network Coding (NC) in collaborative downloading (CD). We focus on the R2V part of CD, and analytically derive probability distribution and the expected value of the amount of time necessary to deliver all of the information to the vehicles with and without NC. Our results show that using NC slightly improves the downloading time in addition to removing the need for having any sort of uplink communications from vehicles to the infrastructure.

Connected through hyperedge HE-1 (induced by source paper "simulation study on collaborative content distribution in delay tolerant vehicular networks"):
  with members:
    - "wireless network coding via modified 802 11 mac phy design and implementation on sdr"

Connected through hyperedge HE-2 (induced by source paper "v2x content distribution based on batched network coding with distributed scheduling"):
  with members:
    - "batched sparse codes"

Connected through hyperedge HE-3 (induced by source paper "wireless network coding via modified 802 11 mac phy design and implementation on sdr"):
  with members:
    - "minimum cost multicast over coded packet networks"
    - "on coding for reliable communication over packet networks"

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)