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: "x sram enabling in memory boolean computations in cmos static random access memories"
  Abstract: Silicon-based static random access memories (SRAM) and digital Boolean logic have been the workhorse of the state-of-the-art computing platforms. Despite tremendous strides in scaling the ubiquitous metal-oxide-semiconductor transistor, the underlying  von-Neumann  computing architecture has remained unchanged. The limited throughput and energy-efficiency of the state-of-the-art computing systems, to a large extent, result from the well-known  von-Neumann bottleneck . The energy and throughput inefficiency of the von-Neumann machines have been accentuated in recent times due to the present emphasis on data-intensive applications such as artificial intelligence, machine learning, and cryptography. A possible approach towards mitigating the overhead associated with the von-Neumann bottleneck is to enable  in-memory  Boolean computations. In this paper, we present an augmented version of the conventional SRAM bit-cells, called  the X-SRAM , with the ability to perform in-memory, vector Boolean computations, in addition to the usual memory storage operations. We propose at least six different schemes for enabling in-memory vector computations, including NAND, NOR, IMP (implication), XOR logic gates, with respect to different bit-cell topologies − the 8T cell and the 8+T Differential cell. In addition, we also present a novel  ‘read-compute-store’  scheme, wherein the computed Boolean function can be directly stored in the memory without the need of latching the data and carrying out a subsequent write operation. The feasibility of the proposed schemes has been verified using the predictive transistor models and detailed Monte-Carlo variation analysis. As an illustration, we also present the efficacy of the proposed in-memory computations by implementing advanced encryption standard algorithm on a non-standard von-Neumann machine wherein the conventional SRAM is replaced by X-SRAM. Our simulations indicated that up to 75% of memory accesses can be saved using the proposed techniques.

Connected through hyperedge HE-1 (induced by source paper "eva cim a system level performance and energy evaluation framework for computing in memory architectures"):
  with members:
    - "computing in memory with spin transfer torque magnetic ram"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"

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:
    - "red a reram based deconvolution accelerator"
    - "mixed precision in memory computing"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "slim simultaneous logic in memory computing exploiting bilayer analog oxram devices"):
  with members:
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "minimum energy quantized neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "memory and information processing in neuromorphic systems"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-4 (induced by source paper "8t sram cell as a multi bit dot product engine for beyond von neumann computing"):
  with members:
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "the power of sparsity in 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)