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: "fast ids computing system method and its memristor crossbar based hardware implementation"
  Abstract: Active Learning Method (ALM) is one of the powerful tools in soft computing that is inspired by human brain capabilities in processing complicated information. ALM, which is in essence an adaptive fuzzy learning method, models a Multi-Input Single-Output (MISO) system with several Single-Input Single-Output (SISO) subsystems. Ink Drop Spread (IDS) operator, which is the main processing engine of this method, extracts useful features from the data without complicated computations and provides stability and convergence as well. Despite great performance of ALM in applications such as classification, clustering, and modelling, an efficient hardware implementation has remained a challenging problem. Large amount of memory required to store the information of IDS planes as well as the high computational cost of the IDS computing system are two main barriers to ALM becoming more popular. In this paper, a novel learning method is proposed based on the idea of IDS, but with a novel approach that eliminates the computational cost of IDS operator. Unlike traditional approaches, our proposed method finds functions to describe the IDS plane that eliminates the need for large amount of memory to a great extent. Narrow Path and Spread, which are two main features used in the inference engine of ALM, are then extracted from IDS planes with minimum amount of memory usage and power consumption. Our proposed algorithm is fully compatible with memristor-crossbar implementation that leads to a significant decrease in the number of required memristors (from O(n^2) to O(3n)). Simpler algorithm and higher speed make our algorithm suitable for applications where real-time process, low-cost and small implementation are paramount. Applications in clustering and function approximation are provided, which reveals the effective performance of our proposed algorithm.

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.ET(Emerging Technologies)