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: "we can remember it for you wholesale implications of data remanence on the use of ram for true random number generation on rfid tags rfidsec 2009"
  Abstract: Random number generation is a fundamental security primitive for RFID devices. However, even this relatively simple requirement is beyond the capacity of today's average RFID tag. A recently proposed solution, Fingerprint Extraction and Random Number Generation in SRAM (FERNS) [14, 15], involves the use of onboard RAM as the source of "true" randomness. Unfortunately, practical considerations prevent this approach from reaching its full potential. First, this method must compete with other system functionalities for use of memory. Thus, the amount of uninitialized RAM available for utilization as a randomness generator may be severely restricted. Second, RAM is subject to data remanence; there is a time period after losing power during which stored data remains intact in memory. This means that after a portion of memory has been used for entropy collection once it will require a relatively extended period of time without power before it can be reused. In a usable RFID based security application, which requires multiple or long random numbers, this may lead to unacceptably high delays. #R##N#In this paper, we show that data remanence negatively affects RAM based random number generation. We demonstrate the practical considerations that must be taken into account when using RAM as an entropy source. We also discuss the implementation of a true random number generator on Intel's WISP RFID tag, which is the first such implementation to the authors' best knowledge. By relating this to the requirements of some popular RFID authentication protocols, we assess the (im)practicality of utilizing memory based randomness techniques on resource constrained devices.

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)