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: "throughput and delay analysis of wireless random access networks"
  Abstract: This paper studies the network throughput and transport delay of a multihop wireless random access network based on a Markov renewal model of packet transportation. We show that the distribution of the source-to-destination (SD) distance plays a critical role in characterizing network performance. We establish necessary and sufficient condition on the SD distance for scalable network throughput, and address the optimal rate allocation issue with fairness and the QoS requirements taken into consideration. In respect to the end-to-end performance, the transport delay is explored in this paper along with network throughput. We characterize the transport delay by relating it to nodal queueing behavior and the SD-distance distribution; the former is a local property while the latter is a global property. In addition, we apply the large deviation theory to derive the tail distribution of transport delay. To put our theory into practical network operation, several traffic scaling laws are provided to demonstrate how network scalability can be achieved by localizing the traffic pattern, and a leaky bucket scheme at the network access is proposed for traffic shaping and flow control.

Connected through hyperedge HE-1 (induced by source paper "capacity of large scale csma wireless networks"):
  with members:
    - "effective carrier sensing in csma networks under cumulative interference"
    - "safe carrier sensing range in csma network under physical interference model"
    - "rethinking information theory for mobile ad hoc networks"
    - "back of the envelope computation of throughput distributions in csma wireless networks"
    - "throughput scaling in random wireless networks a non hierarchical multipath routing strategy"
    - "hierarchical cooperation achieves optimal capacity scaling in ad hoc 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)