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: "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach"
  Abstract: The emerging ultra-dense small cell networks (UD-SCNs) will need to combat a variety of challenges. On the one hand, massive number of devices sharing the limited wireless resources renders centralized control mechanisms infeasible due to the excessive cost of information acquisition and computation. On the other hand, to reduce the energy consumption from fixed power grid and/or battery, network entities (e.g., small cell base stations and user devices) may need to rely on the energy harvested from the ambient environment (e.g., from environmental sources). However, opportunistic energy harvesting introduces uncertainty in the network operation. In this paper, we study the distributed user association problem for energy harvesting UD-SCNs. After reviewing the state-of-the-art research, we outline the major challenges that arise in the presence of energy harvesting due to the uncertainty (e.g., limited knowledge on energy harvesting process or channel profile) as well as limited computational capacities. Finally, we propose an approach based on the mean-field multi-armed bandit games to solve the uplink user association problem for energy harvesting devices in a UD-SCN in the presence of uncertainty.

Connected through hyperedge HE-1 (induced by source paper "learning and fairness in energy harvesting a maximin multi armed bandits approach"):
  with members:
    - "online learning schemes for power allocation in energy harvesting communications"
    - "energy harvesting wireless communications a review of recent advances"
    - "throughput optimization for massive mimo systems powered by wireless energy transfer"
    - "user cooperation in wireless powered communication networks"
    - "wireless networks with rf energy harvesting a contemporary survey"
    - "throughput maximization in wireless powered communication networks"

Connected through hyperedge HE-2 (induced by source paper "risk averse explore then commit algorithms for finite time bandits"):
  with members:
    - "risk aware multi armed bandits using conditional value at risk"
    - "blind gb pandas a blind throughput optimal load balancing algorithm for affinity scheduling"
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "near data scheduling for data centers with multiple levels of data locality"
    - "stochastic multi armed bandits in constant space"
    - "risk averse multi armed bandit problems under mean variance measure"
    - "a survey on contextual multi armed bandits"
    - "exploration vs exploitation vs safety risk averse multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "application of machine learning in wireless networks key techniques and open issues"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep learning for radio resource allocation in multi cell networks"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"

Connected through hyperedge HE-4 (induced by source paper "collaborative artificial intelligence ai for user cell association in ultra dense cellular systems"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "optimal transport theory for cell association in uav enabled cellular networks"
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "learning how to communicate in the internet of things finite resources and heterogeneity"

Connected through hyperedge HE-5 (induced by source paper "distributed user association in energy harvesting small cell networks an exchange economy with uncertainty"):
  with members:
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "user association in 5g networks a survey and an outlook"
    - "cooperative interference mitigation and handover management for heterogeneous cloud small cell networks"
    - "optimizing user association and spectrum allocation in hetnets a utility perspective"
    - "distributed channel assignment in cognitive radio networks stable matching and walrasian equilibrium"
    - "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-6 (induced by source paper "cognitive hierarchy theory for distributed resource allocation in the internet of things"):
  with members:
    - "learning with finite memory for machine type communication"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"

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)