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: "matching with externalities for context aware user cell association in small cell networks"
  Abstract: In this paper, we propose a novel user-cell association approach for wireless small cell networks that exploits previously unexplored context information extracted from users' devices, i.e., user equipments (UEs). Beyond characterizing precise quality of service (QoS) requirements that accurately reflect the UEs' application usage, our proposed cell association approach accounts for the devices' hardware type (e.g., smartphone, tablet, laptop). This approach has the practical benefit of enabling the small cells to make better informed cell association decisions that handle practical device-specific QoS characteristics. We formulate the problem as a matching game between small cell base stations (SBSs) and UEs. In this game, the SBSs and UEs rank one another based on well-designed utility functions that capture composite QoS requirements, extracted from the context features (i.e., application in use, hardware type). We show that the preferences used by the nodes to rank one another are interdependent and influenced by the existing network-wide matching. Due to this unique feature of the preferences, we show that the proposed game can be classified as a many-to-one matching game with externalities. To solve this game, we propose a distributed algorithm that enables the players (i.e., UEs and SBSs) to self-organize into a stable matching that guarantees the required applications' QoS. Simulation results show that the proposed context-aware cell association scheme yields significant gains, reaching up to 52% improvement compared to baseline context-unaware approaches.

Connected through hyperedge HE-1 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "on the energy and data storage management in energy harvesting wireless communications"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "wireless network intelligence at the edge"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"

Connected through hyperedge HE-2 (induced by source paper "contract based incentive mechanism for lte over unlicensed channels"):
  with members:
    - "orla olaa orthogonal coexistence of laa and wifi in unlicensed spectrum"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "the impact of unlicensed access on small cell resource allocation"
    - "breaking the economic barrier of caching in cellular networks incentives and contracts"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "auction based coopetition between lte unlicensed and wi fi"

Connected through hyperedge HE-3 (induced by source paper "spectrum matching in licensed spectrum sharing"):
  with members:
    - "two sided matching based cooperative spectrum sharing"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "sub channel assignment power allocation and user scheduling for non orthogonal multiple access networks"):
  with members:
    - "collaborative smartphone sensing using overlapping coalition formation games"
    - "matching theory for priority based cell association in the downlink of wireless small cell networks"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"
    - "on the complexity of joint subcarrier and power allocation for multi user ofdma systems"
    - "peer effects and stability in matching markets"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-5 (induced by source paper "matching theory for future wireless networks fundamentals and applications"):
  with members:
    - "distributed medium access control for energy efficient transmission in cognitive radios"
    - "many to many matching games for proactive social caching in wireless small cell networks"
    - "distributed energy efficient channel allocation"

Connected through hyperedge HE-6 (induced by source paper "matching theory for priority based cell association in the downlink of wireless small cell networks"):
  with members:
    - "user association for load balancing in heterogeneous cellular networks"
    - "analytical evaluation of fractional frequency reuse for heterogeneous 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)