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: "brain like associative learning using a nanoscale non volatile phase change synaptic device array"
  Abstract: Recent advances in neuroscience together with nanoscale electronic device technology have resulted in huge interests in realizing brain-like computing hardwares using emerging nanoscale memory devices as synaptic elements. Although there has been experimental work that demonstrated the operation of nanoscale synaptic element at the single device level, network level studies have been limited to simulations. In this work, we demonstrate, using experiments, array level associative learning using phase change synaptic devices connected in a grid like configuration similar to the organization of the biological brain. Implementing Hebbian learning with phase change memory cells, the synaptic grid was able to store presented patterns and recall missing patterns in an associative brain-like fashion. We found that the system is robust to device variations, and large variations in cell resistance states can be accommodated by increasing the number of training epochs. We illustrated the tradeoff between variation tolerance of the network and the overall energy consumption, and found that energy consumption is decreased significantly for lower variation tolerance.

Connected through hyperedge HE-1 (induced by source paper "x changr changing memristive crossbar mapping for mitigating line resistance induced accuracy degradation in deep neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "neuromorphic computing with multi memristive synapses"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "8t sram cell as a multi bit dot product engine for beyond von neumann computing"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "xcel ram accelerating binary neural networks in high throughput sram compute arrays"
    - "group scissor scaling neuromorphic computing design to large neural networks"
    - "trannsformer n eural n etwork transform ation for memristive crossbar based neuromorphic system design"

Connected through hyperedge HE-2 (induced by source paper "a photonic in memory computing primitive for spiking neural networks using phase change materials"):
  with members:
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"

Connected through hyperedge HE-3 (induced by source paper "physical reservoir computing built by spintronic devices for temporal information processing"):
  with members:
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-4 (induced by source paper "neuromorphic computing with multi memristive synapses"):
  with members:
    - "mixed precision in memory computing"
    - "temporal correlation detection using computational phase change memory"
    - "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"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-5 (induced by source paper "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"):
  with members:
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "deep learning in neural networks"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-6 (induced by source paper "biological plausibility and stochasticity in scalable vo2 active memristor neurons"):
  with members:
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-7 (induced by source paper "neuron inspired data encoding memristive multi level memory cell"):
  with members:
    - "multi level memristive memory with resistive networks"

Connected through hyperedge HE-8 (induced by source paper "multi level memristive memory with resistive networks"):
  with members:
    - "a survey of memristive threshold logic circuits"

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.NE(Neural and Evolutionary Computing)