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 deep reinforcement learning based framework for content caching"
  Abstract: Content caching at the edge nodes is a promising technique to reduce the data traffic in next-generation wireless networks. Inspired by the success of Deep Reinforcement Learning (DRL) in solving complicated control problems, this work presents a DRL-based framework with Wolpertinger architecture for content caching at the base station. The proposed framework is aimed at maximizing the long-term cache hit rate, and it requires no knowledge of the content popularity distribution. To evaluate the proposed framework, we compare the performance with other caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU), and First-In First-Out (FIFO) caching strategies. Meanwhile, since the Wolpertinger architecture can effectively limit the action space size, we also compare the performance with Deep Q-Network to identify the impact of dropping a portion of the actions. Our results show that the proposed framework can achieve improved short-term cache hit rate and improved and stable long-term cache hit rate in comparison with LRU, LFU, and FIFO schemes. Additionally, the performance is shown to be competitive in comparison to Deep Q-learning, while the proposed framework can provide significant savings in runtime.

Connected through hyperedge HE-1 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning"):
  with members:
    - "ieee 802 11be extremely high throughput the next generation of wi fi technology beyond 802 11ax"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "deep reinforcement learning for resource management in network slicing"
    - "experience driven networking a deep reinforcement learning based approach"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "progressive neural architecture search"
    - "hindsight experience replay"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-2 (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"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "modeling analysis and optimization of coded caching in small cell networks"
    - "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 "viewport aware deep reinforcement learning approach for 360 o video caching"):
  with members:
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "cost effective cache deployment in mobile heterogeneous networks"
    - "saltinet scan path prediction on 360 degree images using saliency volumes"
    - "edge caching wireless networks performance analysis and optimization"
    - "deep reinforcement learning framework applications and embedded implementations"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "caching at the wireless edge design aspects challenges and future directions"
    - "deep reinforcement learning in large discrete action spaces"

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"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "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"
    - "caching policy for cache enabled d2d communications by learning user preference"
    - "multi antenna coded caching"

Connected through hyperedge HE-5 (induced by source paper "optimal caching designs for perfect imperfect and unknown file popularity distributions in large scale multi tier wireless networks"):
  with members:
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "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-6 (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"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-7 (induced by source paper "smartchoices hybridizing programming and machine learning"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "competitive caching with machine learned advice"
    - "learning memory access patterns"
    - "addressing function approximation error in actor critic methods"
    - "kickstarting deep reinforcement learning"
    - "the case for learned index structures"
    - "neural combinatorial optimization with reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "deep multi agent reinforcement learning based cooperative edge caching in wireless networks"):
  with members:
    - "learning based delay aware caching in wireless d2d caching networks"
    - "decentralized caching for content delivery based on blockchain a game theoretic perspective"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "optimal dynamic multicast scheduling for cache enabled content centric 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.IT(Information Theory)