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: "edgedash exploiting network assisted adaptive video streaming for edge caching"
  Abstract: While edge video caching has great potential to decrease the core network traffic as well as the users' experienced latency, it is often challenging to exploit the caches in current client-driven video streaming solutions due to two key reasons. First, even those clients interested in the same content might request different quality levels as a video content is encoded into multiple qualities to match a wide range of network conditions and device capabilities. Second, the clients, who select the quality of the next chunk to request, are unaware of the cached content at the network edge. Hence, it becomes imperative to develop network-side solutions to exploit caching. This can also mitigate some performance issues, in particular for the scenarios in which multiple video clients compete for some bottleneck capacity. In this paper, we propose a network-side control logic running at a WiFi AP to facilitate the use of cached video content. In particular, an AP can assign a client station a different video quality than its request, in case the alternative quality provides a better utility. We formulate the quality assignment problem as an optimization problem and develop several heuristics with polynomial complexity. Compared to the baseline where the clients determine the quality adaptation, our proposals, referred to as EdgeDASH, offer higher video quality, higher cache hits, and lower stalling ratio which are essential for user's satisfaction. Our simulations show that EdgeDASH facilitates significant cache hits and decreases the buffer stalls only by changing the client's request by one quality level. Moreover, from our analysis, we conclude that the network assistance provides significant performance improvement, especially when the clients with identical interests compete for a bottleneck link's capacity.

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)