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: "multi armed bandits with application to 5g small cells"
  Abstract: Due to the pervasive demand for mobile services, next generation wireless networks are expected to be able to deliver high data rates while wireless resources become more and more scarce. This requires the next generation wireless networks to move toward new networking paradigms that are able to efficiently support resource-demanding applications such as personalized mobile services. Examples of such paradigms foreseen for the emerging 5G cellular networks include very densely deployed small cells and device-to-device communications. For 5G networks, it will be imperative to search for spectrum and energy-efficient solutions to the resource allocation problems that i) are amenable to distributed implementation, ii) are capable of dealing with uncertainty and lack of information, and iii) can cope with users’ selfishness. The core objective of this article is to investigate and establish the potential of the MAB framework to address this challenge. In particular, we provide a brief tutorial on bandit problems, including different variations and solution approaches. Furthermore, we discuss recent applications as well as future research directions. In addition, we provide a detailed example of using an MAB model for energy-efficient small cell activation in 5G networks.

Connected through hyperedge HE-1 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-2 (induced by source paper "multi armed bandit for energy efficient and delay sensitive edge computing in dynamic networks with uncertainty"):
  with members:
    - "computation offloading and activation of mobile edge computing servers a minority game"
    - "thompson sampling for budgeted multi armed bandits"
    - "optimization of radio and computational resources for energy efficiency in latency constrained application offloading"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"):
  with members:
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "random access schemes in wireless systems with correlated user activity"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"

Connected through hyperedge HE-4 (induced by source paper "wireless optimisation via convex bandits unlicensed lte wifi coexistence"):
  with members:
    - "introduction to online convex optimization"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "fair coexistence of scheduled and random access wireless networks unlicensed lte wifi"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "analysis and optimization of caching and multicasting in large scale cache enabled heterogeneous wireless networks"
    - "combinatorial bandits revisited"
    - "unlicensed lte wifi coexistence is lbt inherently fairer than csat"

Connected through hyperedge HE-5 (induced by source paper "multi player multi armed bandit based resource allocation for d2d communications"):
  with members:
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"

Connected through hyperedge HE-6 (induced by source paper "distributed user association in energy harvesting small cell networks a probabilistic bandit model"):
  with members:
    - "user association in 5g networks a survey and an outlook"
    - "multi access communications with energy harvesting a multi armed bandit model and the optimality of the myopic policy"
    - "cooperative interference mitigation and handover management for heterogeneous cloud small cell networks"
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
    - "a context aware matching game for user association in wireless small cell networks"
    - "matching theory for priority based cell association in the downlink of wireless small cell networks"
    - "a traffic load balancing framework for software defined radio access networks powered by hybrid energy sources"

Connected through hyperedge HE-7 (induced by source paper "context aware proactive content caching with service differentiation in wireless networks"):
  with members:
    - "a non stochastic learning approach to energy efficient mobility management"
    - "big data meets telcos a proactive caching perspective"
    - "distributed online learning in social recommender systems"
    - "learning based optimization of cache content in a small cell base station"
    - "content level selective offloading in heterogeneous networks multi armed bandit optimization and regret bounds"
    - "cache enabled small cell networks modeling and tradeoffs"
    - "living on the edge the role of proactive caching in 5g wireless networks"
    - "content aware user clustering and caching in wireless small cell networks"

Connected through hyperedge HE-8 (induced by source paper "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach"):
  with members:
    - "distributed wireless power transfer with energy feedback"
    - "distributed user association in energy harvesting small cell networks an exchange economy with uncertainty"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "user association in 5g networks a survey and an outlook"
    - "optimum transmission policies for energy harvesting sensor networks powered by a mobile control center"
    - "sum rate maximizing cell association via dual connectivity"
    - "multicast aware caching for small cell networks"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"

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.LG(Machine Learning)