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 learning policies for power allocation in multiple access channels"
  Abstract: We analyze the problem of distributed power allocation for orthogonal multiple access channels by considering a continuous non-cooperative game whose strategy space represents the users' distribution of transmission power over the network's channels. When the channels are static, we find that this game admits an exact potential function and this allows us to show that it has a unique equilibrium almost surely. Furthermore, using the game's potential property, we derive a modified version of the replicator dynamics of evolutionary game theory which applies to this continuous game, and we show that if the network's users employ a distributed learning scheme based on these dynamics, then they converge to equilibrium exponentially quickly. On the other hand, a major challenge occurs if the channels do not remain static but fluctuate stochastically over time, following a stationary ergodic process. In that case, the associated ergodic game still admits a unique equilibrium, but the learning analysis becomes much more complicated because the replicator dynamics are no longer deterministic. Nonetheless, by employing results from the theory of stochastic approximation, we show that users still converge to the game's unique equilibrium. #R##N#Our analysis hinges on a game-theoretical result which is of independent interest: in finite player games which admit a (possibly nonlinear) convex potential function, the replicator dynamics (suitably modified to account for nonlinear payoffs) converge to an eps-neighborhood of an equilibrium at time of order O(log(1/eps)).

Connected through hyperedge HE-1 (induced by source paper "low complexity distributed radio access network slicing algorithms and experimental results"):
  with members:
    - "equilibrium computation in atomic splittable singleton congestion games"
    - "from network sharing to multi tenancy the 5g network slice broker"
    - "network slicing games enabling customization in multi tenant networks"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"
    - "a survey on 5g the next generation of mobile communication"
    - "distance distributions for real cellular networks"
    - "emerging technologies and research challenges for 5g wireless networks"
    - "altruism in atomic congestion games"

Connected through hyperedge HE-2 (induced by source paper "a learning approach for low complexity optimization of energy efficiency in multicarrier wireless networks"):
  with members:
    - "a brief introduction to machine learning for engineers"
    - "energy efficient power control a look at 5g wireless technologies"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "learning to be green robust energy efficiency maximization in dynamic mimo ofdm systems"
    - "transmit without regrets online optimization in mimo ofdm cognitive radio systems"
    - "framework for link level energy efficiency optimization with informed transmitter"
    - "energy efficient power control in impulse radio uwb wireless networks"

Connected through hyperedge HE-3 (induced by source paper "interference coordination via power domain channel estimation"):
  with members:
    - "ultra dense small cell networks turning density into energy efficiency"
    - "coordination in distributed networks via coded actions with application to power control"
    - "resource allocation for energy efficient 3 way relay channels"
    - "compressive rate estimation with applications to device to device communications"
    - "energy aware competitive power allocation for heterogeneous networks under qos constraints"
    - "crawford sobel meet lloyd max on the grid"
    - "coded power control performance analysis"
    - "power optimization in random wireless networks"

Connected through hyperedge HE-4 (induced by source paper "learning in an uncertain world mimo covariance matrix optimization with imperfect feedback"):
  with members:
    - "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "transmit without regrets online optimization in mimo ofdm cognitive radio systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "regularization techniques for learning with matrices"
    - "the mimo iterative waterfilling algorithm"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-5 (induced by source paper "resource allocation in a mac with and without security via game theoretic learning"):
  with members:
    - "learning equilibria of a stochastic game on gaussian interference channels with incomplete information"
    - "achievable secrecy sum rate in a fading mac wt with power control and without csi of eavesdropper"
    - "on the secure dof of the single antenna mac"

Connected through hyperedge HE-6 (induced by source paper "a comprehensive survey of potential game approaches to wireless networks"):
  with members:
    - "centralized distributed spectrum access for small cell networks a cloud based game solution"
    - "decentralized computation offloading game for mobile cloud computing"
    - "database assisted spectrum access in dynamic networks a distributed learning solution"
    - "joint channel selection and power control in infrastructureless wireless networks a multiplayer multiarmed bandit framework"
    - "distributed learning algorithms for spectrum sharing in spatial random access wireless networks"
    - "spatial spectrum access game"
    - "joint access point selection and power allocation for uplink wireless networks"
    - "database assisted distributed spectrum sharing"

Connected through hyperedge HE-7 (induced by source paper "game theory for signal processing in networks"):
  with members:
    - "understanding game theory via wireless power control"
    - "crawford sobel meet lloyd max on the grid"
    - "coalitional games in miso interference channels epsilon core and coalition structure stable set"
    - "multiobjective signal processing optimization the way to balance conflicting metrics in 5g systems"
    - "distributed cooperative sensing in cognitive radio networks an overlapping coalition formation approach"
    - "distributed adaptive networks a graphical evolutionary game theoretic view"
    - "jamming games in the mimo wiretap channel with an active eavesdropper"
    - "on the nash equilibria in decentralized parallel interference channels"

Connected through hyperedge HE-8 (induced by source paper "coordination in distributed networks via coded actions with application to power control"):
  with members:
    - "a cross layer approach for distributed energy efficient power control in interference networks"
    - "coded power control performance analysis"
    - "strong coordination over a line network"
    - "empirical coordination in a triangular multiterminal network"
    - "distributed channel synthesis"
    - "strong coordination with polar codes"
    - "coordination via a relay"
    - "action dependent strictly causal state communication"

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)