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: "cognitive hierarchy theory for distributed resource allocation in the internet of things"
  Abstract: In this paper, the problem of distributed resource allocation is studied for an Internet of Things (IoT) system, composed of a heterogeneous group of nodes compromising both machine-type devices (MTDs) and human-type devices (HTDs). The problem is formulated as a noncooperative game between the heterogeneous IoT devices that seek to find the optimal time allocation so as to meet their quality-of-service (QoS) requirements in terms of energy, rate and latency. Since the strategy space of each device is dependent on the actions of the other devices, the generalized Nash equilibrium (GNE) solution is first characterized, and the conditions for uniqueness of the GNE are derived. Then, to explicitly capture the heterogeneity of the devices, in terms of resource constraints and QoS needs, a novel and more realistic game-theoretic approach, based on the behavioral framework of cognitive hierarchy (CH) theory, is proposed. This approach is then shown to enable the IoT devices to reach a CH equilibrium (CHE) concept that takes into account the various levels of rationality corresponding to the heterogeneous computational capabilities and the information accessible for each one of the MTDs and HTDs. Simulation results show that the proposed CHE solution keeps the percentage of devices with satisfied QoS constraints above 96% for IoT networks containing up to 10,000 devices without considerably degrading the overall system performance.

Connected through hyperedge HE-1 (induced by source paper "resource management techniques for cloud based iot environment"):
  with members:
    - "a study of efficient energy management techniques for cloud computing environment"
    - "focan a fog supported smart city network architecture for management of applications in the internet of everything environments"
    - "allocation of heterogeneous resources of an iot device to flexible services"
    - "a relative study of task scheduling algorithms in cloud computing environment"
    - "flexible allocation of heterogeneous resources to services on an iot device"

Connected through hyperedge HE-2 (induced by source paper "minimum age of information in the internet of things with non uniform status packet sizes"):
  with members:
    - "average peak age of information minimization in uav assisted iot networks"
    - "age optimal transmission of rateless codes in an erasure channel"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "a reinforcement learning approach to age of information in multi user networks"
    - "can decentralized status update achieve universally near optimal age of information in wireless multiaccess channels"
    - "sampling for data freshness optimization non linear age functions"
    - "controlling packet drops to improve freshness of information"
    - "joint status sampling and updating for minimizing age of information in the internet of things"

Connected through hyperedge HE-3 (induced by source paper "distributed uplink power control in an ultra dense millimeter wave network a mean field game approach"):
  with members:
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "energy efficient user association and power allocation in millimeter wave based ultra dense networks with energy harvesting base stations"
    - "millimeter wave cellular wireless networks potentials and challenges"

Connected through hyperedge HE-4 (induced by source paper "multimedia crowdsourcing with bounded rationality a cognitive hierarchy perspective"):
  with members:
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "reputation based incentive protocols in crowdsourcing applications"

Connected through hyperedge HE-5 (induced by source paper "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"):
  with members:
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "random access schemes in wireless systems with correlated user activity"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "virtual reality over wireless networks quality of service model and learning based resource management"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "grant free massive noma outage probability and throughput"

Connected through hyperedge HE-6 (induced by source paper "deep learning for signal authentication and security in massive internet of things systems"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "colonel blotto game for secure state estimation in interdependent critical infrastructure"
    - "proactive resource management in lte u systems a deep learning perspective"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-7 (induced by source paper "collaborative artificial intelligence ai for user cell association in ultra dense cellular systems"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "echo state networks for self organizing resource allocation in lte u with uplink downlink decoupling"
    - "optimal transport theory for cell association in uav enabled cellular networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "evolutionary games for correlation aware clustering in massive machine to machine networks"):
  with members:
    - "an evolutionary game for user access mode selection in fog radio access networks"
    - "stochastic coalitional games for cooperative random access in m2m communications"
    - "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"

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)