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: "fashion functional and attack graph secured hybrid optimization of virtualized networks"
  Abstract: Maintaining a resilient computer network is a delicate task with conflicting priorities. Flows should be served while controlling risk due to attackers. Configuration is time intensive and largely static until a major new vulnerability forces change. Tools exist to check network reachability (Khurshid et al., NSDI 2013) and risk using (probabilistic) attack graphs (Sheyner et al., IEEE S\&P 2002). However, these tools are not designed to fashion configurations that simultaneously satisfy multiple properties. #R##N#We introduce FASHION: a linear optimizer that fashions network configurations that balance functionality and security requirements. FASHION formalizes functionality as a multi-commodity flow problem with side-constraints. FASHION's primary technical contribution is formulating an approximation of network risk that can be solved using a binary integer program. The approximation linearly combines two measures. One measure is the impact of the set of nodes the attacker can reach in the attack graph (ignoring probability). The second is the maximum probability path in the attack graph. #R##N#FASHION is evaluated on data center networks. The evaluation synthesizes attack graphs on the fat tree topology with up to 128 hosts and 81 network devices. FASHION usually outputs a solution in under 10 minutes, allowing response to short term changes in functionality or security. Solutions are monotonic for all observed experiments: as one increases weight on the security objective, the actual risk, as evaluated by a probabilistic attack graph analysis, never increases. #R##N#FASHION outputs a set of software-defined networking rules consumable by a Frenetic controller (Foster et al., ICFP 2011). FASHION allows an enterprise to automatically reconfigure their network upon a change in functionality (shift in user demand) or security (publication or patching of a vulnerability).

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)