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: "offloading in software defined network at edge with information asymmetry a contract theoretical approach"
  Abstract: The proliferation of highly capable mobile devices such as smartphones and tablets has significantly increased the demand for wireless access. Software defined network (SDN) at edge is viewed as one promising technology to simplify the traffic offloading process for current wireless networks. In this paper, we investigate the incentive problem in SDN-at-edge of how to motivate a third party access points (APs) such as WiFi and smallcells to offload traffic for the central base stations (BSs). The APs will only admit the traffic from the BS under the precondition that their own traffic demand is satisfied. Under the information asymmetry that the APs know more about own traffic demands, the BS needs to distribute the payment in accordance with the APs' idle capacity to maintain a compatible incentive. First, we apply a contract-theoretic approach to model and analyze the service trading between the BS and APs. Furthermore, other two incentive mechanisms: optimal discrimination contract and linear pricing contract are introduced to serve as the comparisons of the anti adverse selection contract. Finally, the simulation results show that the contract can effectively incentivize APs' participation and offload the cellular network traffic. Furthermore, the anti adverse selection contract achieves the optimal outcome under the information asymmetry scenario.

Connected through hyperedge HE-1 (induced by source paper "incentive design for efficient federated learning in mobile networks a contract theory approach"):
  with members:
    - "rewarding high quality data via influence functions"
    - "federated machine learning concept and applications"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "efficient training management for mobile crowd machine learning a deep reinforcement learning approach"
    - "federated learning for mobile keyboard prediction"
    - "multi objective evolutionary federated learning"
    - "blockchained on device federated learning"
    - "biscotti a ledger for private and secure peer to peer machine learning"

Connected through hyperedge HE-2 (induced by source paper "permissioned blockchain for efficient and secure resource sharing in vehicular edge computing"):
  with members:
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "a survey of distributed consensus protocols for blockchain networks"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "end to end performance analysis of relay cooperative communication based on parked cars"
    - "blockchain a distributed solution to automotive security and privacy"

Connected through hyperedge HE-3 (induced by source paper "optimal pricing based edge computing resource management in mobile blockchain"):
  with members:
    - "a survey on security and privacy issues of bitcoin"
    - "cloud fog computing resource management and pricing for blockchain networks"
    - "performance analysis and application of mobile blockchain"

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.NI(Networking and Internet Architecture)