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: "shared spectrum access communications a neutral host micro operator approach"
  Abstract: In this paper, we conceive an advanced small cells wireless network deployment framework within a managed space under shared spectrum access paradigm. We also conceive a complementary business model, referred to as neutral host micro operator (NH-   $\mu \text{O}$   ), that leverages a single shared wireless infrastructure to mutually benefit    $\mu \text{O}$    (a third party service provider), the owner of the space/facility, and mobile network operators (MNOs). The model is composed of a    $\mu \text{O}$    slice, which delivers a venue with customized wireless services tailored to the its local service requirements, and an MNO slice, which facilitates improved wireless coverage to visitors/end-users with subscriptions to several different MNOs. The NH-   $\mu \text{O}$    is not biased to favor any specific client, which can be enforced through a concrete commercial agreement. A radio access network slicing concept is exploited to support and optimize both the slice instances (SIs) efficiently in a shared manner on a single physical network infrastructure. In addition, we devise an efficient architecture for the NH-   $\mu \text{O}$    small cell base station and dynamic spectrum assignment control unit, and their required functionalities supporting sustainable coexistence of different SIs in shared spectrum. We also devise both inter-SI and intra-SI dynamic spectrum allocation policies considering time-varying requirements of different SIs. These policies take care of application level priority by providing a proper mixture of users with guaranteed quality of service and best-effort users, while ensuring a healthy SI competition. The advantages of the proposed framework are twofold. It enables the venue owner to manage its wireless networks and consider its very specific requirements while capitalizing from the MNO slice. This, in turn, reduces the need for deployment of new infrastructure while providing improved wireless coverage and savings to MNOs. Finally, our proposed framework leverages efficient utilization of spectrum, physical infrastructure, and computational resources. The simulation results exhibit various important features of the proposed shared spectrum access policies.

Connected through hyperedge HE-1 (induced by source paper "iris deep reinforcement learning driven shared spectrum access architecture for indoor neutral host small cells"):
  with members:
    - "deepmind control suite"
    - "on spectrum sharing among micro operators in 5g"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "resource allocation in shared spectrum access communications for operators with diverse service requirements"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "co primary inter operator spectrum sharing over a limited spectrum pool using repeated games"

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)