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: "a cluster based replication architecture for load balancing in peer to peer content distribution"
  Abstract: In P2P systems, large volumes of data are declustered naturally across a large number of peers. But it is very difficult to control the initial data distribution because every user has the freedom to share any data with other users. The system scalability can be improved by distributing the load across multiple servers which is proposed by replication. The large scale content distribution systems were improved broadly using the replication techniques. The demanded contents can be brought closer to the clients by multiplying the source of information geographically, which in turn reduce both the access latency and the network traffic. In addition to this, due to the intrinsic dynamism of the P2P environment, static data distribution cannot be expected to guarantee good load balancing. If the hot peers become bottleneck, it leads to increased user response time and significant performance degradation of the system. Hence an effective load balancing mechanism is necessary in such cases and it can be attained efficiently by intelligent data replication. In this paper, we propose a cluster based replication architecture for load-balancing in peer-to-peer content distribution systems. In addition to an intelligent replica placement technique, it also consists of an effective load balancing technique. In the intelligent replica placement technique, peers are grouped into strong and weak clusters based on their weight vector which comprises available capacity, CPU speed, access latency and memory size. In order to achieve complete load balancing across the system, an intracluster and inter-cluster load balancing algorithms are proposed. We are able to show that our proposed architecture attains less latency and better throughput with reduced bandwidth usage, through the simulation results.

Connected through hyperedge HE-1 (induced by source paper "a weighted combination similarity measure for mobility patterns in wireless networks"):
  with members:
    - "cluster based load balancing algorithms for grids"
    - "fast k means algorithm clustering"

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)