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: "aerial uav iot sensing for ubiquitous immersive communication and virtual human teleportation"
  Abstract: We consider UAV IoT aerial sensing that delivers multiple VR/AR immersive communication sessions to remote users. The UAV swarm is spatially distributed over a wide area of interest, and each UAV captures a viewpoint of the scene below it. The remote users are interested in visual immersive navigation of specific subareas/scenes of interest, reconstructed on their respective VR/AR devices from the captured data. The reconstruction quality of the immersive scene representations at the users will depend on the sampling/sensing rates associated with each UAV. There is a limit on the aggregate amount of data that the UAV swarm can sample and send towards the users, stemming from physical/transmission capacity constraints. Similarly, each VR/AR application has minimum reconstruction quality requirements for its own session. We propose an optimization framework that makes three contributions in this context. First, we select the optimal sampling rates to be used by each UAV, such that the system and application constraints are not exceed, while the priority weighted reconstruction quality across all VR/AR sessions is maximized. Then, we design an optimal scalable source-channel signal representation that instills into the captured data inherent rate adaptivity, unequal error protection, and minimum required redundancy. Finally, the UAV transmission efficiency is enhanced by the use of small-form-factor multi-beam directional antennas and optimal power/link scheduling across the scalable signal representation layers. Our experiments demonstrate competitive advantages over conventional methods for visual sensing. This is a first-of-its-kind study of an emerging application of prospectively broad societal impact.

Connected through hyperedge HE-1 (induced by source paper "wildfire monitoring in remote areas using autonomous unmanned aerial vehicles"):
  with members:
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
    - "autonomous extraction of a hierarchical structure of tasks in reinforcement learning a sequential associate rule mining approach"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "on greedy routing in dynamic uav networks"
    - "a unified framework for joint mobility prediction and object profiling of drones in uav networks"

Connected through hyperedge HE-2 (induced by source paper "accelerated structure aware reinforcement learning for delay sensitive energy harvesting wireless sensors"):
  with members:
    - "structural properties of optimal transmission policies for delay sensitive energy harvesting wireless sensors"
    - "viewport adaptive navigable 360 degree video delivery"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "fast reinforcement learning for energy efficient wireless communication"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"
    - "optimal packet scheduling in an energy harvesting communication system"

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)