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: "channel selection for network assisted d2d communication via no regret bandit learning with calibrated forecasting"
  Abstract: We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum, but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources form a distributed system where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Theoretical analysis shows that the proposed approach not only yields vanishing regret in comparison to the global optimal solution but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.

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"
    - "collaborative spatial reuse in wireless networks via selfish multi armed bandits"
    - "ieee 802 11ax high efficiency wlans"
    - "decentralized ap selection in large scale wireless lans considering multi ap interference"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-2 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-3 (induced by source paper "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"):
  with members:
    - "resource allocation for solar powered uav communication systems"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "indoor mobile coverage problem using uavs"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "efficient 3d placement of a uav using particle swarm optimization"
    - "placement optimization of uav mounted mobile base stations"

Connected through hyperedge HE-4 (induced by source paper "20 years of evolution from cognitive to intelligent communications"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep learning in physical layer communications"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-5 (induced by source paper "potential and pitfalls of multi armed bandits for decentralized spatial reuse in wlans"):
  with members:
    - "to overlap or not to overlap enabling channel bonding in high density wlans"
    - "dynamic channel bonding in spatially distributed high density wlans"
    - "performance analysis of dynamic channel bonding in spatially distributed high density wlans"
    - "implications of decentralized q learning resource allocation in wireless networks"
    - "ieee 802 11ax high efficiency wlans"
    - "multi player bandits a musical chairs approach"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-6 (induced by source paper "decentralized ap selection using multi armed bandits opportunistic epsilon greedy with stickiness"):
  with members:
    - "the tmb path loss model for 5 ghz indoor wifi scenarios on the empirical relationship between rssi mcs and spatial streams"
    - "ieee 802 11ax high efficiency wlans"
    - "decentralized ap selection in large scale wireless lans considering multi ap interference"

Connected through hyperedge HE-7 (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-8 (induced by source paper "multi player multi armed bandit based resource allocation for d2d communications"):
  with members:
    - "multi armed bandits with application to 5g small cells"

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.GT(Computer Science and Game Theory)