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: "a reinforcement learning approach to proactive caching in wireless networks"
  Abstract: We consider a mobile user accessing contents in a dynamic environment, where new contents are generated over time (by the user's contacts), and remain relevant to the user for random lifetimes. The user, equipped with a finite-capacity cache memory, randomly accesses the system, and requests all the relevant contents at the time of access. The system incurs an energy cost associated with the number of contents downloaded and the channel quality at that time. Assuming causal knowledge of the channel quality, the content profile, and the user-access behavior, we model the proactive caching problem as a Markov decision process with the goal of minimizing the long-term average energy cost. We first prove the optimality of a threshold-based proactive caching scheme, which dynamically caches or removes appropriate contents from the memory, prior to being requested by the user, depending on the channel state. The optimal threshold values depend on the system state, and hence, are computationally intractable. Therefore, we propose parametric representations for the threshold values, and use reinforcement-learning algorithms to find near-optimal parametrizations. We demonstrate through simulations that the proposed schemes significantly outperform classical reactive downloading, and perform very close to a genie-aided lower bound.

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"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "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 "optimal pricing of internet of things a machine learning approach"):
  with members:
    - "hybrid pricing for mobile collaborative internet access"
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "mobile big data analytics using deep learning and apache spark"
    - "context aware computing for the internet of things a survey"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "a theory of pricing private data"
    - "a survey of smart data pricing past proposals current plans and future trends"

Connected through hyperedge HE-3 (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"
    - "jointly optimal routing and caching for arbitrary network topologies"

Connected through hyperedge HE-4 (induced by source paper "learning to code coded caching via deep reinforcement learning"):
  with members:
    - "an approximation algorithm for optimal clique cover delivery in coded caching"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "deep learning based caching for self driving car in multi access edge computing"
    - "caching in combination networks novel multicast message generation and delivery by leveraging the network topology"
    - "fundamental tradeoff between storage and latency in cache aided wireless interference networks"
    - "a deep reinforcement learning based framework for content caching"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "multi antenna coded caching"

Connected through hyperedge HE-5 (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"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "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"
    - "deep reinforcement learning a brief survey"
    - "a deep reinforcement learning based framework for content caching"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "learning to cache with no regrets"):
  with members:
    - "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"
    - "jointly optimal routing and caching for arbitrary network topologies"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "placing dynamic content in caches with small population"
    - "optimal geographic caching in cellular networks"
    - "adapting caching to audience retention rate which video chunk to store"

Connected through hyperedge HE-8 (induced by source paper "learning to cooperate in d2d caching networks"):
  with members:
    - "learning to cache with no regrets"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "a low complexity approach to distributed cooperative caching with geographic constraints"
    - "spatial multi lru distributed caching for wireless networks with coverage overlaps"
    - "wireless caching technical misconceptions and business barriers"
    - "a learning based approach to caching in heterogenous small cell networks"
    - "efficient and fair collaborative mobile internet access"

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.SY(Systems and Control)