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: "two sided matching based cooperative spectrum sharing"
  Abstract: Dynamic spectrum access (DSA) can effectively improve the spectrum efficiency and alleviate the spectrum scarcity, by allowing unlicensed secondary users (SUs) to access the licensed spectrum of primary users (PUs) opportunistically. Cooperative spectrum sharing is a new promising paradigm to provide necessary  incentives  for both PUs and SUs in dynamic spectrum access. The key idea is that SUs relay the traffic of PUs in exchange for the access time on the PUs’ licensed spectrum. In this paper, we formulate the cooperative spectrum sharing between multiple PUs and multiple SUs as a  two-sided market , and study the market equilibrium under both complete and incomplete information. First, we characterize the sufficient and necessary conditions for the market equilibrium. We analytically show that there may exist multiple market equilibria, among which there is always a unique   Pareto-optimal  equilibrium for PUs (called    $\sf{PU\hbox{-}Optimal\hbox{-}EQ}$      ), in which  every  PU achieves a utility no worse than in any other equilibrium. Then, we show that under complete information, the unique Pareto-optimal equilibrium    $\sf{PU\hbox{-}Optimal\hbox{-}EQ}$       can always be achieved despite the competition among PUs; whereas, under incomplete information, the    $\sf{PU\hbox{-}Optimal\hbox{-}EQ}$       may not be achieved due to the mis-representations of SUs (in reporting their private information). Regarding this, we further study the worse-case equilibrium for PUs, and characterize a  Robust   equilibrium for PUs (called   $\sf{PU\hbox{-}Robust\hbox{-}EQ}$      ), which provides  every  PU a guaranteed utility under all possible mis-representation behaviors of SUs. Numerical results show that in a typical network where the number of PUs and SUs are different, the performance gap between   $\sf{PU\hbox{-}Optimal\hbox{-}EQ}$       and    $\sf{PU\hbox{-}Robust\hbox{-}EQ}$       is quite small (e.g., less than 10 percent in the simulations).

Connected through hyperedge HE-1 (induced by source paper "context aware group buying in ultra dense small cell networks unity is strength"):
  with members:
    - "energy group buying with loading sharing for green cellular networks"
    - "load optimization with user association in cooperative and load coupled lte networks"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "5g ultra dense cellular networks"
    - "coalitional games with overlapping coalitions for interference management in small cell networks"

Connected through hyperedge HE-2 (induced by source paper "spectrum matching in licensed spectrum sharing"):
  with members:
    - "census tract license areas disincentive for sharing the 3 5ghz band"
    - "blma a blind matching algorithm with application to cognitive radio networks"
    - "energy aware cooperative wireless networks with multiple cognitive users"
    - "matching theory for future wireless networks fundamentals and applications"
    - "matching with externalities for context aware user cell association in small cell networks"

Connected through hyperedge HE-3 (induced by source paper "data centric mobile crowdsensing"):
  with members:
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "economic analysis of crowdsourced wireless community networks"
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "dynamic routing for social information sharing"
    - "providing long term participation incentive in participatory sensing"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"
    - "motivating smartphone collaboration in data acquisition and distributed computing"

Connected through hyperedge HE-4 (induced by source paper "on optimal power allocation for downlink non orthogonal multiple access systems"):
  with members:
    - "distributed optimization of hierarchical small cell networks a gnep framework"
    - "a survey of downlink non orthogonal multiple access for 5g wireless communication networks"
    - "optimal joint power and subcarrier allocation for full duplex multicarrier non orthogonal multiple access systems"
    - "energy efficient transmission design in non orthogonal multiple access"
    - "dynamic user clustering and power allocation for uplink and downlink non orthogonal multiple access noma systems"
    - "the application of mimo to non orthogonal multiple access"
    - "fairness for non orthogonal multiple access in 5g systems"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"

Connected through hyperedge HE-5 (induced by source paper "achieving an efficient and fair equilibrium through taxation"):
  with members:
    - "economic analysis of crowdsourced wireless community networks"
    - "public wi fi monetization via advertising"
    - "efficient and fair collaborative mobile internet access"
    - "an integrated spectrum and information market for green cognitive communications"
    - "mine gold to deliver green cognitive communications"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"
    - "the robust price of anarchy of altruistic games"
    - "stable nash equilibria of medium access games under symmetric socially altruistic behavior"

Connected through hyperedge HE-6 (induced by source paper "intelligent power control for spectrum sharing a deep reinforcement learning approach"):
  with members:
    - "deep learning for wireless physical layer opportunities and challenges"
    - "retrodirective multi user wireless power transfer with massive mimo"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

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)