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: "temporal correlation detection using computational phase change memory"
  Abstract: Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems. New computing paradigms, such as in-memory computing, are expected to overcome the limitations of conventional computing approaches. Sebastian et al. report a large-scale demonstration of computational phase change memory (PCM) by performing high-level computational primitives using one million PCM devices.

Connected through hyperedge HE-1 (induced by source paper "mixed precision deep learning based on computational memory"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "training lstm networks with resistive cross point devices"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "an analysis of neural language modeling at multiple scales"
    - "an improved evaluation framework for generative adversarial networks"

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"
    - "x sram enabling in memory boolean computations in cmos static random access memories"
    - "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 "memristors for the curious outsiders"):
  with members:
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "hierarchical temporal memory using memristor networks a survey"
    - "thermodynamic ram technology stack"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "vertex coloring of graphs via phase dynamics of coupled oscillatory networks"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "optimal dictionary for least squares representation"

Connected through hyperedge HE-4 (induced by source paper "in memory computing on a photonic platform"):
  with members:
    - "mixed precision in memory computing"
    - "superconducting optoelectronic circuits for neuromorphic computing"
    - "mixed precision memcomputing"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"
    - "universal memcomputing machines"
    - "the parallel approach"

Connected through hyperedge HE-5 (induced by source paper "neuromorphic computing with multi memristive synapses"):
  with members:
    - "mixed precision in memory computing"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-6 (induced by source paper "mixed precision in memory computing"):
  with members:
    - "mixed precision training of deep neural networks using computational memory"
    - "universal memcomputing machines"
    - "changing computing paradigms towards power efficiency"
    - "the parallel approach"

Connected through hyperedge HE-7 (induced by source paper "x sram enabling in memory boolean computations in cmos static random access memories"):
  with members:
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "computing in memory with spin transfer torque magnetic ram"

Connected through hyperedge HE-8 (induced by source paper "freestyle a randomized version of chacha for resisting offline brute force and dictionary attacks"):
  with members:
    - "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)