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: "stochastic coalitional games for cooperative random access in m2m communications"
  Abstract: In this paper, the problem of random access contention between machine type devices (MTDs) in the uplink of a wireless cellular network is studied. In particular, the possibility of forming cooperative groups to coordinate the MTDs' requests for the random access channel (RACH) is analyzed. The problem is formulated as a stochastic coalition formation game in which the MTDs are the players that seek to form cooperative coalitions to optimize a utility function that captures each MTD's energy consumption and time-varying queue length. Within each coalition, an MTD acts as a coalition head that sends the access requests of the coalition members over the RACH. One key feature of this game is its ability to cope with stochastic environments in which the arrival requests of MTDs and the packet success rate over RACH are dynamically time-varying. The proposed stochastic coalitional is composed of multiple stages, each of which corresponds to a coalitional game in stochastic characteristic form that is played by the MTDs at each time step. To solve this game, a novel distributed coalition formation algorithm is proposed and shown to converge to a stable MTD partition. Simulation results show that, on the average, the proposed stochastic coalition formation algorithm can reduce the average fail ratio and energy consumption of up to 36% and 31% for a cluster-based distribution of MTDs, respectively, compared with a noncooperative case. Moreover, when the MTDs are more sensitive to the energy consumption (queue length), the coalitions' size will increase (decrease).

Connected through hyperedge HE-1 (induced by source paper "ran slicing for massive iot and bursty urllc service multiplexing analysis and optimization"):
  with members:
    - "how should i orchestrate resources of my slices for bursty urllc service provision"
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "random access analysis for massive iot networks under a new spatio temporal model a stochastic geometry approach"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control"
    - "downlink capacity and base station density in cellular networks"

Connected through hyperedge HE-2 (induced by source paper "evolutionary games for correlation aware clustering in massive machine to machine networks"):
  with members:
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "an evolutionary game for user access mode selection in fog radio access networks"
    - "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"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "distance distributions in finite uniformly random networks theory and applications"

Connected through hyperedge HE-3 (induced by source paper "random access analysis for massive iot networks under a new spatio temporal model a stochastic geometry approach"):
  with members:
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "artificial noise aided secure transmission in large scale spectrum sharing networks"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "physical layer security in three tier wireless sensor networks a stochastic geometry approach"
    - "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control"
    - "analytical modeling of uplink cellular networks"
    - "internet of things iot a vision architectural elements and future directions"
    - "downlink capacity and base station density in cellular networks"

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)