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: "an experimental investigation of hyperbolic routing with a smart forwarding plane in ndn"
  Abstract: Routing in NDN networks must scale in terms of forwarding table size and routing protocol overhead. Hyperbolic routing (HR) presents a potential solution to address the routing scalability problem, because it does not use traditional forwarding tables or exchange routing updates upon changes in network topologies. Although HR has the drawbacks of producing sub-optimal routes or local minima for some destinations, these issues can be mitigated by NDN's intelligent data forwarding plane. However, HR's viability still depends on both the quality of the routes HR provides and the overhead incurred at the forwarding plane due to HR's sub-optimal behavior. We designed a new forwarding strategy called Adaptive Smoothed RTT-based Forwarding (ASF) to mitigate HR's sub-optimal path selection. This paper describes our experimental investigation into the packet delivery delay and overhead under HR as compared with Named-Data Link State Routing (NLSR), which calculates shortest paths. We run emulation experiments using various topologies with different failure scenarios, probing intervals, and maximum number of next hops for a name prefix. Our results show that HR's delay stretch has a median close to 1 and a 95th-percentile around or below 2, which does not grow with the network size. HR's message overhead in dynamic topologies is nearly independent of the network size, while NLSR's overhead grows polynomially at least. These results suggest that HR offers a more scalable routing solution with little impact on the optimality of routing paths.

Connected through hyperedge HE-1 (induced by source paper "link persistence and conditional distances in multiplex networks"):
  with members:
    - "a dynamic network model with persistent links and node specific latent variables with an application to the interbank market"
    - "network mapping by replaying hyperbolic growth"
    - "spreading processes in multilayer networks"
    - "on the diameter of hyperbolic random graphs"
    - "network geometry inference using common neighbors"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-2 (induced by source paper "geohyperbolic routing and addressing schemes"):
  with members:
    - "greedy routing and the algorithmic small world phenomenom"
    - "generating massive complex networks with hyperbolic geometry faster in practice"
    - "sampling geometric inhomogeneous random graphs in linear time"
    - "network mapping by replaying hyperbolic growth"
    - "geometric inhomogeneous random graphs"
    - "network geometry inference using common neighbors"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-3 (induced by source paper "greedy routing and the algorithmic small world phenomenom"):
  with members:
    - "average distance in a general class of scale free networks with underlying geometry"
    - "network mapping by replaying hyperbolic growth"
    - "geometric inhomogeneous random graphs"
    - "on the diameter of hyperbolic random graphs"
    - "four degrees of separation"
    - "sustaining the internet with hyperbolic mapping"
    - "networks become navigable as nodes move and forget"
    - "on compact routing for the internet"

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)