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: "computing in memory with spin transfer torque magnetic ram"
  Abstract: In-memory computing is a promising approach to addressing the processor-memory data transfer bottleneck in computing systems. We propose Spin-Transfer Torque Compute-in-Memory (STT-CiM), a design for in-memory computing with Spin-Transfer Torque Magnetic RAM (STT-MRAM). The unique properties of spintronic memory allow multiple wordlines within an array to be simultaneously enabled, opening up the possibility of directly sensing functions of the values stored in multiple rows using a single access. We propose modifications to STT-MRAM peripheral circuits that leverage this principle to perform logic, arithmetic, and complex vector operations. We address the challenge of reliable in-memory computing under process variations by extending ECC schemes to detect and correct errors that occur during CiM operations. We also address the question of how STT-CiM should be integrated within a general-purpose computing system. To this end, we propose architectural enhancements to processor instruction sets and on-chip buses that enable STT-CiM to be utilized as a scratchpad memory. Finally, we present data mapping techniques to increase the effectiveness of STT-CiM. We evaluate STT-CiM using a device-to-architecture modeling framework, and integrate cycle-accurate models of STT-CiM with a commercial processor and on-chip bus (Nios II and Avalon from Intel). Our system-level evaluation shows that STT-CiM provides system-level performance improvements of 3.93x on average (upto 10.4x), and concurrently reduces memory system energy by 3.83x on average (upto 12.4x).

Connected through hyperedge HE-1 (induced by source paper "a new mram based process in memory accelerator for efficient neural network training with floating point precision"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "imagenet training in minutes"
    - "critical learning periods in deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "eva cim a system level performance and energy evaluation framework for computing in memory architectures"):
  with members:
    - "x sram enabling in memory boolean computations in cmos static random access memories"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"

Connected through hyperedge HE-3 (induced by source paper "tim dnn ternary in memory accelerator for deep neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "pact parameterized clipping activation for quantized neural networks"
    - "wrpn wide reduced precision networks"
    - "ternary neural networks with fine grained quantization"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "effective quantization methods for recurrent neural networks"
    - "trained ternary quantization"

Connected through hyperedge HE-4 (induced by source paper "an overview of in memory processing with emerging non volatile memory for data intensive applications"):
  with members:
    - "red a reram based deconvolution accelerator"
    - "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"
    - "graphr accelerating graph processing using reram"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "valley coupled spintronic non volatile memories with compute in memory support"):
  with members:
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-6 (induced by source paper "noisy computations during inference harmful or helpful"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "densely connected convolutional networks"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "deep learning in neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "xcel ram accelerating binary neural networks in high throughput sram compute arrays"):
  with members:
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "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-8 (induced by source paper "training recurrent neural networks against noisy computations during inference"):
  with members:
    - "lstm a search space odyssey"
    - "towards evaluating the robustness of neural networks"
    - "improving the robustness of deep neural networks via stability training"
    - "deep learning in 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)