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: "learning with finite memory for machine type communication"
  Abstract: Machine-type devices (MTDs) will lie at the heart of the Internet of Things (IoT) system. A key challenge in such a system is sharing network resources between small MTDs, which have limited memory and computational capabilities. In this paper, a novel learning \emph{with finite memory} framework is proposed to enable MTDs to effectively learn about each others message state, so as to properly adapt their transmission parameters. In particular, an IoT system in which MTDs can transmit both delay tolerant, periodic messages and critical alarm messages is studied. For this model, the characterization of the exponentially growing delay for critical alarm messages and the convergence of the proposed learning framework in an IoT are analyzed. Simulation results show that the delay of critical alarm messages is significantly reduced up to $94\%$ with very minimal memory requirements. The results also show that the proposed learning with finite memory framework is very effective in mitigating the limiting factors of learning that prevent proper learning procedures.

Connected through hyperedge HE-1 (induced by source paper "distributed learning for low latency machine type communication in a massive internet of things"):
  with members:
    - "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks"
    - "label less learning for traffic control in an edge network"
    - "lte lte a random access for massive machine type communications in smart cities"
    - "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"
    - "what can wireless cellular technologies do about the upcoming smart metering traffic"
    - "reliable reporting for massive m2m communications with periodic resource pooling"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"

Connected through hyperedge HE-2 (induced by source paper "cognitive hierarchy theory for distributed resource allocation in the internet of things"):
  with members:
    - "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "fundamentals of throughput maximization with random arrivals for m2m communications"

Connected through hyperedge HE-3 (induced by source paper "learning how to communicate in the internet of things finite resources and heterogeneity"):
  with members:
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "mobile internet of things can uavs provide an energy efficient mobile architecture"
    - "towards massive ultra reliable and low latency wireless communication with short packets"
    - "wireless communications in the era of big data"
    - "towards massive machine type cellular communications"
    - "towards a consumer centric grid a behavioral perspective"
    - "what can wireless cellular technologies do about the upcoming smart metering traffic"

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)