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: "accurate learning or fast mixing dynamic adaptability of caching algorithms"
  Abstract: Typical analysis of content caching algorithms using the metric of hit probability under a stationary request process does not account for performance loss under a variable request arrival process. In this work, we consider adaptability of caching algorithms from two perspectives: (a) the accuracy of learning a fixed popularity distribution; and (b) the speed of learning items' popularity. In order to attain this goal, we compute the distance between the stationary distributions of several popular algorithms with that of a genie-aided algorithm that has knowledge of the true popularity ranking, which we use as a measure of learning accuracy. We then characterize the mixing time of each algorithm, i.e., the time needed to attain the stationary distribution, which we use as a measure of learning efficiency. We merge both measures above to obtain the "learning error" representing both how quickly and how accurately an algorithm learns the optimal caching distribution, and use this to determine the trade-off between these two objectives of many popular caching algorithms. Informed by the results of our analysis, we propose a novel hybrid algorithm, Adaptive-LRU (A-LRU), that learns both faster and better the changes in the popularity. We show numerically that it also outperforms all other candidate algorithms when confronted with either a dynamically changing synthetic request process or using real world traces.

Connected through hyperedge HE-1 (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-2 (induced by source paper "the study of dynamic caching via state transition field the case of time invariant popularity"):
  with members:
    - "content popularity prediction towards location aware mobile edge caching"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "caching with time varying popularity profiles a learning theoretic perspective"
    - "enabling edge cooperation in tactile internet via 3c resource sharing"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "online edge caching and wireless delivery in fog aided networks with dynamic content popularity"

Connected through hyperedge HE-3 (induced by source paper "the role of caching in future communication systems and networks"):
  with members:
    - "classifying flows and buffer state for youtube s http adaptive streaming service in mobile networks"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "on coding for cache aided delivery of dynamic correlated content"
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "updating content in cache aided coded multicast"
    - "on the effects of subpacketization in content centric mobile networks"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "fog aided wireless networks for content delivery fundamental latency tradeoffs"

Connected through hyperedge HE-4 (induced by source paper "joint data compression and caching approaching optimality with guarantees"):
  with members:
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "what when and where to cache a unified optimization approach"
    - "network cache design under stationary requests exact analysis and poisson approximation"
    - "adaptive caching networks with optimality guarantees"
    - "on the complexity of optimal routing and content caching in heterogeneous networks"

Connected through hyperedge HE-5 (induced by source paper "what when and where to cache a unified optimization approach"):
  with members:
    - "lru cache under stationary requests"
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "network cache design under stationary requests exact analysis and poisson approximation"
    - "a utility optimization approach to network cache design"
    - "adaptive caching networks with optimality guarantees"
    - "on the complexity of optimal routing and content caching in heterogeneous networks"
    - "a unified approach to the performance analysis of caching systems"

Connected through hyperedge HE-6 (induced by source paper "adaptive ttl based caching for content delivery"):
  with members:
    - "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"
    - "catalog dynamics impact of content publishing and perishing on the performance of a lru cache"
    - "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 "network cache design under stationary requests exact analysis and poisson approximation"):
  with members:
    - "on the convergence of the ttl approximation for an lru cache under independent stationary request processes"
    - "lru cache under stationary requests"
    - "what when and where to cache a unified optimization approach"
    - "optimizing timer based policies for general cache networks"
    - "a utility optimization approach to network cache design"
    - "a unified approach to the performance analysis of caching systems"
    - "impact of traffic mix on caching performance in a content centric network"

Connected through hyperedge HE-8 (induced by source paper "optimal energy consumption with communication computation caching and qoi guarantee"):
  with members:
    - "joint data compression and caching approaching optimality with guarantees"
    - "adaptive caching networks with optimality guarantees"

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)