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: "catalog dynamics impact of content publishing and perishing on the performance of a lru cache"
  Abstract: The Internet heavily relies on Content Distribution Networks and transparent caches to cope with the ever-increasing traffic demand of users. Content, however, is essentially versatile: once published at a given time, its popularity vanishes over time. All requests for a given document are then concentrated between the publishing time and an effective perishing time. #R##N#In this paper, we propose a new model for the arrival of content requests, which takes into account the dynamical nature of the content catalog. Based on two large traffic traces collected on the Orange network, we use the semi-experimental method and determine invariants of the content request process. This allows us to define a simple mathematical model for content requests; by extending the so-called "Che approximation", we then compute the performance of a LRU cache fed with such a request process, expressed by its hit ratio. We numerically validate the good accuracy of our model by comparison to trace-based simulation.

Connected through hyperedge HE-1 (induced by source paper "online convex optimization for caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "learning to cooperate in d2d caching networks"
    - "cache optimization models and algorithms"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "jointly optimal routing and caching for arbitrary network topologies"

Connected through hyperedge HE-2 (induced by source paper "cache optimization models and algorithms"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "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"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "a reinforcement learning approach to proactive caching in wireless networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "impact of traffic characteristics on request aggregation in an ndn router"):
  with members:
    - "a versatile and accurate approximation for lru cache performance"

Connected through hyperedge HE-5 (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"
    - "adapting caching to audience retention rate which video chunk to store"
    - "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-6 (induced by source paper "adaptive ttl based caching for content delivery"):
  with members:
    - "accurate learning or fast mixing dynamic adaptability of caching algorithms"
    - "a utility optimization approach to network cache design"
    - "an inverse problem approach for content popularity estimation"
    - "least recently used caches under the shot noise model"
    - "a unified approach to the performance analysis of caching systems"
    - "impact of traffic mix on caching performance in a content centric network"
    - "a versatile and accurate approximation for lru cache performance"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "spatial multi lru distributed caching for wireless networks with coverage overlaps"):
  with members:
    - "wireless caching technical misconceptions and business barriers"
    - "spatial multi lru caching for wireless networks with coverage overlaps"
    - "joint cache channel coding over erasure broadcast channels"
    - "optimal geographic caching in cellular networks"
    - "unravelling the impact of temporal and geographical locality in content caching systems"
    - "modeling and analysis of content caching in wireless small cell networks"
    - "least recently used caches under the shot noise model"
    - "modelling view count dynamics in youtube"

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)