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: "self organized low power iot networks a distributed learning approach"
  Abstract: Enabling large-scale energy-efficient Internet-of-things (IoT) connectivity is an essential step towards realization of networked society. While legacy wide-area wireless systems are highly dependent on network-side coordination, the level of consumed energy in signaling, as well as the expected increase in the number of IoT devices, makes such centralized approaches infeasible in future. Here, we address this problem by self-coordination for IoT networks through learning from past communications. To this end, we first study low-complexity distributed learning approaches applicable in IoT communications. Then, we present a learning solution to adapt communication parameters of devices to the environment for maximizing energy efficiency and reliability in data transmissions. Furthermore, leveraging tools from stochastic geometry, we evaluate the performance of proposed distributed learning solution against the centralized coordination. Finally, we analyze the interplay amongst energy efficiency, reliability of communications against noise and interference over data channel, and reliability against adversarial interference over data and feedback channels. The simulation results indicate that compared to the state of the art approaches, both energy efficiency and reliability in IoT communications could be significantly improved using the proposed learning approach. These promising results, which are achieved using lightweight learning, make our solution favorable in many low-cost low-power IoT applications.

Connected through hyperedge HE-1 (induced by source paper "cellular traffic prediction and classification a comparative evaluation of lstm and arima"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "deep learning for encrypted traffic classification an overview"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

Connected through hyperedge HE-2 (induced by source paper "user traffic prediction for proactive resource management learning powered approaches"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "stock price correlation coefficient prediction with arima lstm hybrid model"
    - "risk aware resource allocation for urllc challenges and strategies with machine learning"
    - "drag deep reinforcement learning based base station activation in heterogeneous networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "neutm a neural network based framework for traffic matrix prediction in sdn"

Connected through hyperedge HE-3 (induced by source paper "gnu radio implementation of malin multi armed bandits learning for internet of things networks"):
  with members:
    - "multi armed bandit learning in iot networks learning helps even in non stationary settings"
    - "low power wide area networks an overview"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "upper confidence bound for channel selection in lpwa networks with retransmissions"):
  with members:
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "low power wide area networks an overview"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "latency energy tradeoff based on channel scheduling and repetitions in nb iot systems"):
  with members:
    - "an enhanced access reservation protocol with a partial preamble transmission mechanism in nb iot systems"
    - "narrowband iot data transmission procedures for massive machine type communications"
    - "evaluating the performance of emtc and nb iot for smart city applications"
    - "optimized lte data transmission procedures for iot device side energy consumption analysis"
    - "random access preamble design and detection for 3gpp narrowband iot systems"
    - "adaptive drx scheme to improve energy efficiency in lte networks with bounded delay"
    - "assessment of lte wireless access for monitoring of energy distribution in the smart grid"

Connected through hyperedge HE-6 (induced by source paper "risk aware resource allocation for urllc challenges and strategies with machine learning"):
  with members:
    - "5g wireless network slicing for embb urllc and mmtc a communication theoretic view"
    - "stochastic optimization and control framework for 5g network slicing with effective isolation"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "a survey on low latency towards 5g ran core network and caching solutions"

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)