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: "what when and where to cache a unified optimization approach"
  Abstract: Caching algorithms are usually described by the eviction method and analyzed using a metric of hit probability. Since contents have different importance (e.g. popularity), the utility of a high hit probability, and the cost of transmission can vary across contents. In this paper, we consider timer-based (TTL) policies across a cache network, where contents have differentiated timers over which we optimize. Each content is associated with a utility measured in terms of the corresponding hit probability. We start our analysis from a linear cache network: we propose a utility maximization problem where the objective is to maximize the sum of utilities and a cost minimization problem where the objective is to minimize the content transmission cost across the network. These frameworks enable us to design online algorithms for cache management, for which we prove achieving optimal performance. Informed by the results of our analysis, we formulate a non-convex optimization problem for a general cache network. We show that the duality gap is zero, hence we can develop a distributed iterative primal-dual algorithm for content management in the network. Finally, we consider two applications of our cache network model: (i) directly mapping to content distribution and (ii) generalization to wireless sensor network by jointly considering content caching and content compression. We characterize the tradeoff among caching, compression and communication via a nonlinear non-convex optimization problem. We show that it can be transformed into an equivalent convex problem. The obtained numerical results provide us with insights into how to optimize the performance.

Connected through hyperedge HE-1 (induced by source paper "pa cache learning based popularity aware content caching in edge networks"):
  with members:
    - "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"
    - "online deep learning learning deep neural networks on the fly"
    - "optimizing timer based policies for general cache networks"
    - "understanding performance of edge content caching for mobile video streaming"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "fractalnet ultra deep neural networks without residuals"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "joint data compression and caching approaching optimality with guarantees"):
  with members:
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "network cache design under stationary requests exact analysis and poisson approximation"
    - "accurate learning or fast mixing dynamic adaptability of caching algorithms"
    - "adaptive caching networks with optimality guarantees"
    - "on the complexity of optimal routing and content caching in heterogeneous networks"

Connected through hyperedge HE-3 (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"
    - "optimizing timer based policies for general cache networks"
    - "accurate learning or fast mixing dynamic adaptability of caching algorithms"
    - "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"

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)