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: "using grouped linear prediction and accelerated reinforcement learning for online content caching"
  Abstract: Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency requires the knowledge of content popularity profile, which however is often unavailable in advance. In this paper, we first propose a new linear prediction model, named grouped linear model (GLM) to estimate the future content requests based on historical data. Unlike many existing works that assumed the static content popularity profile, our model can adapt to the temporal variation of the content popularity in practical systems due to the arrival of new contents and dynamics of user preference. Based on the predicted content requests, we then propose a reinforcement learning approach with model-free acceleration (RLMA) for online cache replacement by taking into account both the cache hits and replacement cost. This approach accelerates the learning process in non-stationary environment by generating imaginary samples for Q-value updates. Numerical results based on real-world traces show that the proposed prediction and learning based online caching policy outperform all considered existing schemes.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based wireless resource allocation with application to vehicular networks"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "multi agent learning for cooperative large scale caching networks"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "edge caching for coverage and capacity aided heterogeneous networks"

Connected through hyperedge HE-2 (induced by source paper "optimal scheduling of content caching subject to deadline"):
  with members:
    - "on optimal proactive and retention aware caching with user mobility"
    - "caching at base stations with heterogeneous user demands and spatial locality"
    - "a rolling horizon dynamic programming approach for collaborative caching"
    - "accounting for information freshness in scheduling of content caching"
    - "caching with time varying popularity profiles a learning theoretic perspective"
    - "optimal geographic caching in cellular networks with linear content coding"
    - "cost optimal caching for d2d networks with user mobility modeling analysis and computational approaches"
    - "a learning based approach to caching in heterogenous small cell 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:
    - "content popularity prediction towards location aware mobile edge 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"
    - "content popularity prediction towards location aware mobile edge 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 "accounting for information freshness in scheduling of content caching"):
  with members:
    - "dynamic content updates in heterogeneous wireless networks"
    - "age of information aware cache updating with file and age dependent update durations"
    - "on optimal proactive and retention aware caching with user mobility"
    - "caching with time varying popularity profiles a learning theoretic perspective"
    - "optimal geographic caching in cellular networks with linear content coding"
    - "cost optimal caching for d2d networks with user mobility modeling analysis and computational approaches"
    - "update or wait how to keep your data fresh"
    - "capacity of cellular networks with femtocache"

Connected through hyperedge HE-6 (induced by source paper "dynamic content updates in heterogeneous wireless networks"):
  with members:
    - "content popularity prediction towards location aware mobile edge 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"

Connected through hyperedge HE-7 (induced by source paper "feedforward neural networks for caching enough or too much"):
  with members:
    - "learning memory access patterns"
    - "multi agent learning for cooperative large scale caching networks"
    - "deep reinforcement learning that matters"
    - "a deep reinforcement learning based framework for content caching"
    - "overcoming catastrophic forgetting in neural 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)