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: "on the limits of predictability in real world radio spectrum state dynamics from entropy theory to 5g spectrum sharing"
  Abstract: A range of applications in cognitive radio networks, from adaptive spectrum sensing to predictive spectrum mobility and dynamic spectrum access, depend on our ability to foresee the state evolution of radio spectrum, raising a fundamental question: To what degree is radio spectrum state (RSS) predictable? In this article we explore the fundamental limits of predictability in RSS dynamics by studying the RSS evolution patterns in spectrum bands of several popular services, including TV bands, ISM bands, cellular bands, and so on. From an information theory perspective, we introduce a methodology of using statistical entropy measures and Fano inequality to quantify the degree of predictability underlying real-world spectrum measurements. Despite the apparent randomness, we find a remarkable predictability, as large as 90 percent, in real-world RSS dynamics over a number of spectrum bands for all popular services. Furthermore, we discuss the potential applications of prediction-based spectrum sharing in 5G wireless communications.

Connected through hyperedge HE-1 (induced by source paper "a survey on low latency towards 5g ran core network and caching solutions"):
  with members:
    - "stochastic optimization and control framework for 5g network slicing with effective isolation"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "human in the loop wireless communications machine learning and brain aware resource management"
    - "r fft function split at ifft fft in unified lte cran and cable access network"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "joint user association power control and scheduling in multi cell 5g networks"
    - "fundamental limits on latency in transceiver cache aided hetnets"
    - "decentralized coded caching in wireless networks trade off between storage and latency"

Connected through hyperedge HE-2 (induced by source paper "cooperative prediction and sensing based spectrum sharing in cognitive radio networks"):
  with members:
    - "an efficient precoder design for multiuser mimo cognitive radio networks with interference constraints"
    - "joint beamforming and antenna selection for sum rate maximization in cognitive radio networks"
    - "robust cooperative spectrum sensing scheduling optimization in multi channel dynamic spectrum access networks"
    - "primary user traffic classification in dynamic spectrum access networks"
    - "joint transmit beamforming and receive power splitting for miso swipt systems"
    - "robust secure transmission in miso channels based on worst case optimization"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "weighted sum rate optimization for cognitive radio mimo broadcast channels"

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)