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: "fpsa a full system stack solution for reconfigurable reram based nn accelerator architecture"
  Abstract: Neural Network (NN) accelerators with emerging ReRAM (resistive random access memory) technologies have been investigated as one of the promising solutions to address the memory wall challenge, due to the unique capability of processing-in-memory within ReRAM-crossbar-based processing elements (PEs). However, the high efficiency and high density advantages of ReRAM have not been fully utilized due to the huge communication demands among PEs and the overhead of peripheral circuits. In this paper, we propose a full system stack solution, composed of a reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and its software system including neural synthesizer, temporal-to-spatial mapper, and placement & routing. We highly leverage the software system to make the hardware design compact and efficient. To satisfy the high-performance communication demand, we optimize it with a reconfigurable routing architecture and the placement & routing tool. To improve the computational density, we greatly simplify the PE circuit with the spiking schema and then adopt neural synthesizer to enable the high density computation-resources to support different kinds of NN operations. In addition, we provide spiking memory blocks (SMBs) and configurable logic blocks (CLBs) in hardware and leverage the temporal-to-spatial mapper to utilize them to balance the storage and computation requirements of NN. Owing to the end-to-end software system, we can efficiently deploy existing deep neural networks to FPSA. Evaluations show that, compared to one of state-of-the-art ReRAM-based NN accelerators, PRIME, the computational density of FPSA improves by 31x; for representative NNs, its inference performance can achieve up to 1000x speedup.

Connected through hyperedge HE-1 (induced by source paper "gemmini an agile systolic array generator enabling systematic evaluations of deep learning architectures"):
  with members:
    - "integrating nvidia deep learning accelerator nvdla with risc v soc on firesim"
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "dnn dataflow choice is overrated"
    - "vta an open hardware software stack for deep learning"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-2 (induced by source paper "mitigate parasitic resistance in resistive crossbar based convolutional neural networks"):
  with members:
    - "memristor based deep convolution neural network a case study"
    - "compression of deep neural networks for image instance retrieval"
    - "ternary weight networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"
    - "going deeper with convolutions"

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)