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: "in memory and error immune differential rram implementation of binarized deep neural networks"
  Abstract: RRAM-based in-Memory Computing is an exciting road for implementing highly energy efficient neural networks. This vision is however challenged by RRAM variability, as the efficient implementation of in-memory computing does not allow error correction. In this work, we fabricated and tested a differential HfO 2 -based memory structure and its associated sense circuitry, which are ideal for in-memory computing. For the first time, we show that our approach achieves the same reliability benefits as error correction, but without any CMOS overhead. We show, also for the first time, that it can naturally implement Binarized Deep Neural Networks, a very recent development of Artificial Intelligence, with extreme energy efficiency, and that the system is fully satisfactory for image recognition applications. Finally, we evidence how the extra reliability provided by the differential memory allows programming the devices in low voltage conditions, where they feature high endurance of billions of cycles.

Connected through hyperedge HE-1 (induced by source paper "stochastic computing for hardware implementation of binarized neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an analysis of deep neural network models for practical applications"
    - "towards accurate binary convolutional neural network"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "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"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-2 (induced by source paper "digital biologically plausible implementation of binarized neural networks with differential hafnium oxide resistive memory arrays"):
  with members:
    - "nvidia tensor core programmability performance precision"
    - "towards accurate binary convolutional neural network"
    - "dark memory and accelerator rich system optimization in the dark silicon era"
    - "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"
    - "memory and information processing in neuromorphic systems"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-3 (induced by source paper "implementing binarized neural networks with magnetoresistive ram without error correction"):
  with members:
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "use of magnetoresistive random access memory as approximate memory for training neural networks"
    - "towards accurate binary convolutional neural network"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "outstanding bit error tolerance of resistive ram based binarized neural networks"):
  with members:
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "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"

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)