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: "coverage and connectivity in three dimensional networks"
  Abstract: Most wireless terrestrial networks are designed based on the assumption that the nodes are deployed on a two-dimensional (2D) plane. However, this 2D assumption is not valid in underwater, atmospheric, or space communications. In fact, recent interest in underwater acoustic ad hoc and sensor networks hints at the need to understand how to design networks in 3D. Unfortunately, the design of 3D networks is surprisingly more difficult than the design of 2D networks. For example, proofs of Kelvin's conjecture and Kepler's conjecture required centuries of research to achieve breakthroughs, whereas their 2D counterparts are trivial to solve. In this paper, we consider the coverage and connectivity issues of 3D networks, where the goal is to find a node placement strategy with 100% sensing coverage of a 3D space, while minimizing the number of nodes required for surveillance. Our results indicate that the use of the Voronoi tessellation of 3D space to create truncated octahedral cells results in the best strategy. In this truncated octahedron placement strategy, the transmission range must be at least 1.7889 times the sensing range in order to maintain connectivity among nodes. If the transmission range is between 1.4142 and 1.7889 times the sensing range, then a hexagonal prism placement strategy or a rhombic dodecahedron placement strategy should be used. Although the required number of nodes in the hexagonal prism and the rhombic dodecahedron placement strategies is the same, this number is 43.25% higher than the number of nodes required by the truncated octahedron placement strategy. We verify by simulation that our placement strategies indeed guarantee ubiquitous coverage. We believe that our approach and our results presented in this paper could be used for extending the processes of 2D network design to 3D networks.

Connected through hyperedge HE-1 (induced by source paper "beyond 5g with uavs foundations of a 3d wireless cellular network"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "uav aided offloading for cellular hotspot"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "uav enabled aerial base station bs iii iii capacity characterization of uav enabled two user broadcast channel"
    - "common throughput maximization in uav enabled ofdma systems with delay consideration"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"

Connected through hyperedge HE-2 (induced by source paper "distributed navigation of multi robot systems for sensing coverage"):
  with members:
    - "voronoi based coverage control of heterogeneous disk shaped robots"
    - "a coverage strategy for wireless sensor networks in a three dimensional environment"
    - "push pull autonomous deployment of mobile sensors for a complete coverage"
    - "extremal properties of three dimensional sensor networks with applications"

Connected through hyperedge HE-3 (induced by source paper "sixsoid a new paradigm for k coverage in 3d wireless sensor networks"):
  with members:
    - "extremal properties of three dimensional sensor networks with applications"

Connected through hyperedge HE-4 (induced by source paper "a coverage strategy for wireless sensor networks in a three dimensional environment"):
  with members:
    - "occupancy grids a stochastic spatial representation for active robot perception"

Connected through hyperedge HE-5 (induced by source paper "on improving the representation of a region achieved by a sensor network"):
  with members:
    - "a new distributed topology control algorithm for wireless environments with non uniform path loss and multipath propagation"

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)