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: "high performance cryptographically secure pseudo random number generation via bitslicing"
  Abstract: In this paper, a high-throughput Cryptographically Secure Pseudo-Random Number Generator (CSPRNG) using the bitslicing technique is proposed. In such technique, instead of the conventional row-major data representation, column-major data representation is employed which allows the bitsliced implementation to take full advantage of all the available datapath of the hardware platform. We use LFSR-based (Linear Feedback Shift Register) PRNG for our implementation since its register oriented architecture perfectly suits the GPU's many-core structure and allows for usage of bitslicing technique which can further improve its performance. In our GPU implementation, each GPU thread is capable of generating a remarkable number of 32 pseudo-random bits in each LFSR clock cycle. In order to obtain cryptographically suitable properties, we propose an SIMD vectorized fully parallel bitsliced implementation of the LFSR-based, cryptographically secure MICKEY 2.0 stream cipher algorithm for CSPRNG. To the best of our knowledge, our method not only achieves better performance, but also significantly outperforms optical solutions in terms of performance per cost while maintaining an acceptable measure of randomness. It should be mentioned that our implementation successfully passes the NIST test for statistical randomness and bitwise correlation criteria. Our proposed methodology significantly outperforms the current best implementations in the literature for computer-based PRNG. Moreover, our evaluations show 6.6x improvement over the Nvidia's proprietary high-performance PRNG, cuRAND library, achieving 5.2 Tb/s of throughput on the affordable Nvidia GTX 980 Ti.

Connected through hyperedge HE-1 (induced by source paper "decentralized cooperative communication less multi agent task assignment with monte carlo tree search"):
  with members:
    - "generating high quality random numbers a high throughput parallel bitsliced approach"
    - "analysis of dynamic task allocation in multi robot systems"

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)