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: "expect more from the networking ddos mitigation by fitt in named data networking"
  Abstract: Distributed Denial of Service (DDoS) attacks have plagued the Internet for decades, but defenses have not fundamentally outpaced attackers. Instead, the size and rate of growth in attacks have actually outpaced carriers' and DDoS mitigation services' growth. In this paper, we comprehensively examine ways in which Named Data Networking (NDN), a proposed data-centric Internet architecture, fundamentally addresses some of the principle weaknesses in today's DDoS defenses in IP networking. We argue that NDN's architectural changes (even when incrementally deployed) can make DDoS attacks fundamentally more difficult to launch and less effective. We present a new DDoS mitigation solution -- Fine-grained Interest Traffic Throttling FITT, to leverage NDN's features to combat DDoS in the Internet of Things (IoT) age. FITT enables the network to detect DDoS directly from feedback from victims, throttle DDoS traffic along its exact path in the network, and perform reinforcement control over the misbehaving entities at their sources. In cases like the Mirai attacks, where smart IoT devices (smart cameras, refrigerators, etc.) were able to cripple high-capacity service providers using diverse DDoS Tactics Techniques and Procedures (TTPs), FITT would be able to precisely squelch the attack traffic at its distributed sources, without disrupting other legitimate application traffic running on the same devices. FITT offers an incrementally deployable solution for service providers to effectuate the application-level remediation at the sources, which remains unattainable in today's DDoS market. Our extensive simulations results show that FITT can effectively throttle attack traffic in a short time and achieve over 99% legitimate traffic.

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.CR(Cryptography and Security)