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: "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
  Abstract: Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this paper, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus, enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

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"
    - "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"
    - "a low complexity approach to distributed cooperative caching with geographic constraints"

Connected through hyperedge HE-2 (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"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "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-3 (induced by source paper "collaborative multi agent multi armed bandit learning for small cell caching"):
  with members:
    - "content popularity prediction towards location aware mobile edge caching"
    - "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-4 (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"
    - "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"
    - "jointly optimal routing and caching for arbitrary network topologies"

Connected through hyperedge HE-5 (induced by source paper "a view on deep reinforcement learning in system optimization"):
  with members:
    - "neural packet classification"
    - "neo a learned query optimizer"
    - "reinforced genetic algorithm learning for optimizing computation graphs"
    - "a survey on compiler autotuning using machine learning"
    - "placeto learning generalizable device placement algorithms for distributed machine learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "seq2sql generating structured queries from natural language using reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "optimal caching designs for perfect imperfect and unknown file popularity distributions in large scale multi tier wireless networks"):
  with members:
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "a deep reinforcement learning based framework for content caching"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "optimization and analysis of probabilistic caching in n tier heterogeneous networks"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "placing dynamic content in caches with small population"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"):
  with members:
    - "joint switch upgrade and controller deployment in hybrid software defined networks"
    - "bandit online learning with unknown delays"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "decentralized caching for content delivery based on blockchain a game theoretic perspective"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "deep reinforcement learning a brief survey"
    - "a deep reinforcement learning based framework for content caching"

Connected through hyperedge HE-8 (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"
    - "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"

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)