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: "dynamic routing for flying ad hoc networks"
  Abstract: This paper reports experimental results on self-organizing wireless networks carried by small flying robots. Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive, and fast to deploy. This makes them a very attractive technology for many civilian and military applications. Due to the high mobility of the nodes, maintaining a communication link between the UAVs is a challenging task. The topology of these networks is more dynamic than that of typical mobile ad hoc networks (MANETs) and of typical vehicle ad hoc networks. As a consequence, the existing routing protocols designed for MANETs partly fail in tracking network topology changes. In this paper, we compare two different routing algorithms for ad hoc networks: optimized link-state routing (OLSR) and predictive OLSR (P-OLSR). The latter is an OLSR extension that we designed for FANETs; it takes advantage of the Global Positioning System (GPS) information available on board. To the best of our knowledge, P-OLSR is currently the only FANET-specific routing technique that has an available Linux implementation. We present results obtained by both media-access-control (MAC) layer emulations and real-world experiments. In the experiments, we used a testbed composed of two autonomous fixed-wing UAVs and a node on the ground. Our experiments evaluate the link performance and the communication range, as well as the routing performance. Our emulation and experimental results show that P-OLSR significantly outperforms OLSR in routing in the presence of frequent network topology changes.

Connected through hyperedge HE-1 (induced by source paper "securing fleets of consumer drones at low cost"):
  with members:
    - "security privacy and safety evaluation of dynamic and static fleets of drones"

Connected through hyperedge HE-2 (induced by source paper "a unified framework for joint mobility prediction and object profiling of drones in uav networks"):
  with members:
    - "on greedy routing in dynamic uav networks"
    - "dronecells improving 5g spectral efficiency using drone mounted flying base stations"
    - "optimal measurement policy for predicting uav network topology"
    - "a coalition formation approach to coordinated task allocation in heterogeneous uav networks"
    - "amateur drone monitoring state of the art architectures key enabling technologies and future research directions"
    - "survey of important issues in uav communication networks"
    - "internet of things applications animal monitoring with unmanned aerial vehicle"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-3 (induced by source paper "on greedy routing in dynamic uav networks"):
  with members:
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "optimal measurement policy for predicting uav network topology"

Connected through hyperedge HE-4 (induced by source paper "robust multi path communications for uavs in the urban iot"):
  with members:
    - "survey of important issues in uav communication networks"

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)