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: "memory slices a modular building block for scalable intelligent memory systems"
  Abstract: While reduction in feature size makes computation cheaper in terms of latency, area, and power consumption, performance of emerging data-intensive applications is determined by data movement. These trends have introduced the concept of scalability as reaching a desirable performance per unit cost by using as few number of units as possible. Many proposals have moved compute closer to the memory. However, these efforts ignored maintaining a balance between bandwidth and compute rate of an architecture, with those of applications, which is a key principle in designing scalable large systems. This paper proposes the use of memory slices, a modular building block for scalable memory systems integrated with compute, in which performance scales with memory size (and volume of data). The slice architecture utilizes a programmable memory interface feeding a systolic compute engine with high reuse rate. The modularity feature of slice-based systems is exploited with a partitioning and data mapping strategy across allocated memory slices where training performance scales with the data size. These features enable shifting the most pressure to cheap compute units rather than expensive memory accesses or transfers via interconnection network. An application of the memory slices to a scale-out memory system is accelerating the training of recurrent, convolutional, and hybrid neural networks (RNNs and RNNs+CNN) that are forming cloud workloads. The results of our cycle-level simulations show that memory slices exhibits a superlinear speedup when the number of slices increases. Furthermore, memory slices improve power efficiency to 747 GFLOPs/J for training LSTMs. While our current evaluation uses memory slices with 3D packaging, a major value is that slices can also be constructed with a variety of packaging options, for example with DDR-based memory units.

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.AR(Hardware Architecture)