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: "incentive mechanism design for wireless energy harvesting based internet of things"
  Abstract: Radio frequency energy harvesting (RFEH) is a promising technology to charge unattended Internet of Things (IoT) low-power devices remotely. To enable this, in future IoT system, besides the traditional data access points (DAPs) for collecting data, energy access points (EAPs) should be deployed to charge IoT devices to maintain their sustainable operations. Practically, the DAPs and EAPs may be operated by different operators, and the DAPs thus need to provide effective incentives to motivate the surrounding EAPs to charge their associated IoT devices. Different from existing incentive schemes, we consider a practical scenario with asymmetric information, where the DAP is not aware of the channel conditions and energy costs of the EAPs. We first extend the existing Stackelberg game-based approach with complete information to the asymmetric information scenario, where the expected utility of the DAP is defined and maximized. To deal with asymmetric information more efficiently, we then develop a contract theory-based framework, where the optimal contract is derived to maximize the DAP's expected utility as well as the social welfare. Simulations show that information asymmetry leads to severe performance degradation for the Stackelberg game-based framework, while the proposed contract theory-based approach using asymmetric information outperforms the Stackelberg game-based method with complete information. This reveals that the performance of the considered system depends largely on the market structure (i.e., whether the EAPs are allowed to optimize their received power at the IoT devices with full freedom or not) than on the information availability (i.e., the complete or asymmetric information).

Connected through hyperedge HE-1 (induced by source paper "joint power splitting and relay selection in energy harvesting communications for iot networks"):
  with members:
    - "when internet of things meets blockchain challenges in distributed consensus"
    - "cooperative ambient backscatter communications for green internet of things"
    - "optimization of a power splitting protocol for two way multiple energy harvesting relay system"
    - "practical non linear energy harvesting model and resource allocation for swipt systems"
    - "optimal cooperative power allocation for energy harvesting enabled relay networks"
    - "outage probability of energy harvesting relay aided cooperative networks over rayleigh fading channel"
    - "power allocation strategies in energy harvesting wireless cooperative networks"
    - "exploiting network cooperation in green wireless communication"

Connected through hyperedge HE-2 (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-3 (induced by source paper "towards secure blockchain enabled internet of vehicles optimizing consensus management using reputation and contract theory"):
  with members:
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "speedychain a framework for decoupling data from blockchain for smart cities"
    - "review of blockchain technology and its expectations case of the energy sector"
    - "intelligent vehicle trust point reward based intelligent vehicle communication using blockchain"
    - "evolutionary game for mining pool selection in blockchain networks"
    - "blockchain based intelligent vehicle data sharing framework"
    - "decentralized computation offloading game for mobile cloud computing"

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.IT(Information Theory)