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 efficient framework for network code based multimedia content distribution in a hybrid p2p network"
  Abstract: Most of the existing P2P content distribution schemes implement a random or rarest piece first dissemination procedure to avoid duplicate transmission of the same pieces of data and rare pieces of data occurring in the network. This problem can be solved using peer-to-peer content distribution based on network coding scheme. Network coding scheme uses random linear combination of coded pieces. Hence the above stated problem can be solved ease and simple. Our proposed mechanism uses network coding mechanism in which several contents of same message is grouped into different group and coding operation is performed only within the same group. The interested peers are also divided into several groups with each group have the responsibility to spread one set of contents of some message. The coding system is designed to assure the property that any subset of the messages can be utilized to decode the original content as long as the size of the subset is suitably large. To meet this condition, dynamic smart network coding scheme is defined which assures the preferred property, then peers are connected in the same group to send the corresponding message, and connect peers in different groups to disseminate messages for carrying out decoding operation. Moreover, the proposed system can be readily expanded to support topology change to get better system performance further in terms of reliability, link stress and throughput. The simulation results prove that the proposed system can attain 20 to 25% higher throughput than existing systems, it also further attains good reliability, link failure and robustness to peer churn.

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)