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: "adapting caching to audience retention rate which video chunk to store"
  Abstract: Rarely do users watch online contents entirely. We study how to take this into account to improve the performance of cache systems for video-on-demand and video-sharing platforms in terms of traffic reduction on the core network. We exploit the notion of "Audience retention rate", introduced by mainstream online content platforms and measuring the popularity of different parts of the same video content. We first characterize the performance limits of a cache able to store parts of videos, when the popularity and the audience retention rate of each video are available to the cache manager. We then relax the assumption of known popularity and we propose a LRU (Least Recently Used) cache replacement policy that operates on the first chunks of each video. We characterize its performance by extending the well-known Che's approximation to this case. We prove that, by refining the chunk granularity, the chunk-LRU policy increases its performance. It is shown numerically that even for a small number of chunks (N=20), the gains of chunk-LRU are still significant in comparison to standard LRU policy that caches entire files, and they are almost optimal.

Connected through hyperedge HE-1 (induced by source paper "learning to cache with no regrets"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-2 (induced by source paper "audience retention rate aware caching and coded video delivery with asynchronous demands"):
  with members:
    - "asynchronous coded caching"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "coded caching and content delivery with heterogeneous distortion requirements"
    - "coded caching for a large number of users"
    - "wireless content caching for small cell and d2d networks"
    - "decentralized caching and coded delivery with distinct cache capacities"
    - "order optimal rate of caching and coded multicasting with random demands"
    - "coded caching for delay sensitive content"

Connected through hyperedge HE-3 (induced by source paper "cache aided interactive multiview video streaming in small cell wireless networks"):
  with members:
    - "mobility and popularity aware coded small cell caching"
    - "joint caching routing and channel assignment for collaborative small cell cellular networks"
    - "optimal layered representation for adaptive interactive multiview video streaming"
    - "learning based optimization of cache content in a small cell base station"
    - "collaborative p2p streaming of interactive live free viewpoint video"
    - "femtocaching wireless video content delivery through distributed caching helpers"

Connected through hyperedge HE-4 (induced by source paper "traffic minimizing caching and latent variable distributions of order statistics"):
  with members:
    - "how much cache is needed to achieve linear capacity scaling in backhaul limited dense wireless networks"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "caching in wireless small cell networks a storage bandwidth tradeoff"
    - "wireless content caching for small cell and d2d networks"
    - "unravelling the impact of temporal and geographical locality in content caching systems"
    - "catalog dynamics impact of content publishing and perishing on the performance of a lru cache"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "base station assisted device to device communications for high throughput wireless video networks"

Connected through hyperedge HE-5 (induced by source paper "design and optimization of vod schemes with client caching in wireless multicast networks"):
  with members:
    - "a learning based approach to caching in heterogenous small cell networks"
    - "the three primary colors of mobile systems"
    - "fundamental limits of caching"

Connected through hyperedge HE-6 (induced by source paper "wireless caching technical misconceptions and business barriers"):
  with members:
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "placing dynamic content in caches with small population"
    - "multi library coded caching"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "analysis on cache enabled wireless heterogeneous networks"
    - "order optimal rate of caching and coded multicasting with random demands"
    - "optimizing mds codes for caching at the edge"
    - "optimal geographic caching in cellular 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)