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: "cellular base station assisted device to device communications in tv white space"
  Abstract: This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a   location-specific   TVWS database, which provides a lookup table   service   for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform   irregular   coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an   opportunistic   spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.

Connected through hyperedge HE-1 (induced by source paper "modeling and analysis of d2d millimeter wave networks with poisson cluster processes"):
  with members:
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "wireless device to device caching networks basic principles and system performance"
    - "modeling and performance analysis of clustered device to device networks"
    - "cooperative non orthogonal multiple access with simultaneous wireless information and power transfer"
    - "d2d enhanced heterogeneous cellular networks with dynamic tdd"
    - "an analytical framework for device to device communication in cellular networks"
    - "28 ghz and 73 ghz millimeter wave indoor propagation measurements and path loss models"
    - "spectrum sharing for device to device communication in cellular networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning power spectrum maps from quantized power measurements"):
  with members:
    - "online learning with multiple operator valued kernels"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-4 (induced by source paper "cognitive network cooperation for green cellular networks"):
  with members:
    - "securing physical layer communications for cognitive radio networks"
    - "exploiting network cooperation in green wireless communication"

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)