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: "content popularity prediction towards location aware mobile edge caching"
  Abstract: Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty}$ filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.

Connected through hyperedge HE-1 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing"):
  with members:
    - "cooperative service caching and workload scheduling in mobile edge computing"
    - "ai assisted low information latency wireless networking"
    - "age of information aware radio resource management in vehicular networks a proactive deep reinforcement learning perspective"
    - "minimum age of information in the internet of things with non uniform status packet sizes"
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "edge caching wireless networks performance analysis and optimization"
    - "edge caching in dense heterogeneous cellular networks with massive mimo aided self backhaul"

Connected through hyperedge HE-2 (induced by source paper "collaborative multi agent multi armed bandit learning for small cell caching"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "a deep reinforcement learning based framework for content caching"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "caching placement in stochastic wireless caching helper networks channel selection diversity via caching"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"

Connected through hyperedge HE-3 (induced by source paper "the study of dynamic caching via state transition field the case of time invariant popularity"):
  with members:
    - "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"
    - "context aware proactive content caching with service differentiation in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "a rolling horizon dynamic programming approach for collaborative caching"):
  with members:
    - "popnetcod a popularity based caching policy for network coding enabled named data networking"
    - "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"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "wireless content caching for small cell and d2d networks"
    - "joint caching routing and channel assignment for collaborative small cell cellular networks"
    - "adaptive prioritized random linear coding and scheduling for layered data delivery from multiple servers"

Connected through hyperedge HE-5 (induced by source paper "dynamic content updates in heterogeneous wireless networks"):
  with members:
    - "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"
    - "mobility and popularity aware coded small cell caching"
    - "understanding performance of edge content caching for mobile video streaming"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "learning based optimization of cache content in a small cell base station"

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)