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: "collaborative spatial reuse in wireless networks via selfish multi armed bandits"
  Abstract: Next-generation wireless deployments are characterized by being dense and uncoordinated, which often leads to inefficient use of resources and poor performance. To solve this, we envision the utilization of completely decentralized mechanisms that enhance Spatial Reuse (SR). In particular, we concentrate in Reinforcement Learning (RL), and more specifically, in Multi-Armed Bandits (MABs), to allow networks to modify both their transmission power and channel based on their experienced throughput. In this work, we study the exploration-exploitation trade-off by means of the $\varepsilon$-greedy, EXP3, UCB and Thompson sampling action-selection strategies. Our results show that optimal proportional fairness can be achieved, even if no information about neighboring networks is available to the learners and WNs operate selfishly. However, there is high temporal variability in the throughput experienced by the individual networks, specially for $\varepsilon$-greedy and EXP3. We identify the cause of this variability to be the adversarial setting of our setup in which the set of most played actions provide intermittent good/poor performance depending on the neighboring decisions. We also show that this variability is reduced using UCB and Thompson sampling, which are parameter-free policies that perform exploration according to the reward distribution of each action.

Connected through hyperedge HE-1 (induced by source paper "multi armed bandits for decentralized ap selection in enterprise wlans"):
  with members:
    - "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"
    - "introduction to multi armed bandits"
    - "the tmb path loss model for 5 ghz indoor wifi scenarios on the empirical relationship between rssi mcs and spatial streams"
    - "a tutorial on thompson sampling"
    - "ieee 802 11ax high efficiency wlans"
    - "decentralized ap selection in large scale wireless lans considering multi ap interference"
    - "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-2 (induced by source paper "spatial reuse in ieee 802 11ax wlans"):
  with members:
    - "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"
    - "to overlap or not to overlap enabling channel bonding in high density wlans"
    - "komondor a wireless network simulator for next generation high density wlans"
    - "dynamic channel bonding in spatially distributed high density wlans"
    - "survey and performance evaluation of the upcoming next generation wlan standard ieee 802 11ax"
    - "optimal resource allocation in ieee 802 11ax uplink ofdma with scheduled access"
    - "mu mimo mac protocols for wireless local area networks a survey"
    - "effective static and adaptive carrier sensing for dense wireless csma networks"

Connected through hyperedge HE-3 (induced by source paper "on the performance of the spatial reuse operation in ieee 802 11ax wlans"):
  with members:
    - "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"
    - "komondor a wireless network simulator for next generation high density wlans"
    - "survey and performance evaluation of the upcoming next generation wlan standard ieee 802 11ax"
    - "the tmb path loss model for 5 ghz indoor wifi scenarios on the empirical relationship between rssi mcs and spatial streams"

Connected through hyperedge HE-4 (induced by source paper "komondor a wireless network simulator for next generation high density wlans"):
  with members:
    - "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"
    - "to overlap or not to overlap enabling channel bonding in high density wlans"
    - "dynamic channel bonding in spatially distributed high density wlans"
    - "ns3 gym extending openai gym for networking research"
    - "ieee 802 11ax high efficiency wlans"
    - "throughput analysis in csma ca networks using continuous time markov networks a tutorial"

Connected through hyperedge HE-5 (induced by source paper "target wake time scheduled access in ieee 802 11ax wlans"):
  with members:
    - "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"
    - "the tmb path loss model for 5 ghz indoor wifi scenarios on the empirical relationship between rssi mcs and spatial streams"
    - "ap initiated multi user transmissions in ieee 802 11ax wlans"
    - "breaking the interference barrier in dense wireless networks with interference alignment"
    - "ieee 802 11ax high efficiency wlans"
    - "traffic differentiation in dense collision free wlans using csma eca"

Connected through hyperedge HE-6 (induced by source paper "towards energy efficient lpwans through learning based multi hop routing for the uplink"):
  with members:
    - "implications of decentralized q learning resource allocation in wireless networks"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

Connected through hyperedge HE-7 (induced by source paper "towards energy efficient lpwans through learning based multi hop routing"):
  with members:
    - "implications of decentralized q learning resource allocation in wireless networks"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

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)