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: "red a reram based deconvolution accelerator"
  Abstract: Deconvolution has been widespread in neural networks. For example, it is essential for performing unsupervised learning in generative adversarial networks or constructing fully convolutional networks for semantic segmentation. Resistive RAM (ReRAM)-based processing-in-memory architecture has been widely explored in accelerating convolutional computation and demonstrates good performance. Performing deconvolution on existing ReRAM-based accelerator designs, however, suffers from long latency and high energy consumption because deconvolutional computation includes not only convolution but also extra add-on operations. To realize the more efficient execution for deconvolution, we analyze its computation requirement and propose a ReRAM-based accelerator design, namely, RED. More specific, RED integrates two orthogonal methods, the pixel-wise mapping scheme for reducing redundancy caused by zero-inserting operations and the zero-skipping data flow for increasing the computation parallelism and therefore improving performance. Experimental evaluations show that compared to the state-of- the-art ReRAM-based accelerator, RED can speed up operation 3.69~31.15× and reduce 8%~88.36% energy consumption.

Connected through hyperedge HE-1 (induced by source paper "hypar towards hybrid parallelism for deep learning accelerator array"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "pt spike a precise time dependent single spike neuromorphic architecture with efficient supervised learning"
    - "ucnn exploiting computational reuse in deep neural networks via weight repetition"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "revisiting small batch training for deep neural networks"
    - "unifying data model and hybrid parallelism in deep learning via tensor tiling"
    - "group scissor scaling neuromorphic computing design to large neural networks"

Connected through hyperedge HE-2 (induced by source paper "an overview of in memory processing with emerging non volatile memory for data intensive applications"):
  with members:
    - "mixed precision in memory computing"
    - "x sram enabling in memory boolean computations in cmos static random access memories"
    - "temporal correlation detection using computational phase change memory"
    - "computing in memory with spin transfer torque magnetic ram"
    - "graphr accelerating graph processing using reram"
    - "xnor net imagenet classification using binary convolutional neural networks"

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)