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: "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
  Abstract: Large scale digital computing almost exclusively relies on the von-Neumann architecture which comprises of separate units for storage and computations. The energy expensive transfer of data from the memory units to the computing cores results in the well-known von-Neumann bottleneck. Various approaches aimed towards bypassing the von-Neumann bottleneck are being extensively explored in the literature. Emerging non-volatile memristive technologies have been shown to be very efficient in computing analog dot products in an in-situ fashion. The memristive analog computation of the dot product results in much faster operation as opposed to digital vector in-memory bit-wise Boolean computations. However, challenges with respect to large scale manufacturing coupled with the limited endurance of memristors have hindered rapid commercialization of memristive based computing solutions. In this work, we show that the standard 8 transistor (8T) digital SRAM array can be configured as an analog-like in-memory multi-bit dot product engine. By applying appropriate analog voltages to the read-ports of the 8T SRAM array, and sensing the output current, an approximate analog-digital dot-product engine can be implemented. We present two different configurations for enabling multi-bit dot product computations in the 8T SRAM cell array, without modifying the standard bit-cell structure. Since our proposal preserves the standard 8T-SRAM array structure, it can be used as a storage element with standard read-write instructions, and also as an on-demand analog-like dot product accelerator.

Connected through hyperedge HE-1 (induced by source paper "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "neuromorphic computing with multi memristive synapses"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"

Connected through hyperedge HE-2 (induced by source paper "xcel ram accelerating binary neural networks in high throughput sram compute arrays"):
  with members:
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "computing in memory with spin transfer torque magnetic ram"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "x sram enabling in memory boolean computations in cmos static random access memories"):
  with members:
    - "temporal correlation detection using computational phase change memory"
    - "computing in memory with spin transfer torque magnetic ram"

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)