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: "enabling the adoption of processing in memory challenges mechanisms future research directions"
  Abstract: Poor DRAM technology scaling over the course of many years has caused DRAM-based main memory to increasingly become a larger system bottleneck. A major reason for the bottleneck is that data stored within DRAM must be moved across a pin-limited memory channel to the CPU before any computation can take place. This requires a high latency and energy overhead, and the data often cannot benefit from caching in the CPU, making it difficult to amortize the overhead. #R##N#Modern 3D-stacked DRAM architectures include a logic layer, where compute logic can be integrated underneath multiple layers of DRAM cell arrays within the same chip. Architects can take advantage of the logic layer to perform processing-in-memory (PIM), or near-data processing. In a PIM architecture, the logic layer within DRAM has access to the high internal bandwidth available within 3D-stacked DRAM (which is much greater than the bandwidth available between DRAM and the CPU). Thus, PIM architectures can effectively free up valuable memory channel bandwidth while reducing system energy consumption. #R##N#A number of important issues arise when we add compute logic to DRAM. In particular, the logic does not have low-latency access to common CPU structures that are essential for modern application execution, such as the virtual memory and cache coherence mechanisms. To ease the widespread adoption of PIM, we ideally would like to maintain traditional virtual memory abstractions and the shared memory programming model. This requires efficient mechanisms that can provide logic in DRAM with access to CPU structures without having to communicate frequently with the CPU. To this end, we propose and evaluate two general-purpose solutions that minimize unnecessary off-chip communication for PIM architectures. We show that both mechanisms improve the performance and energy consumption of many important memory-intensive applications.

Connected through hyperedge HE-1 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "federated multi task learning"

Connected through hyperedge HE-2 (induced by source paper "mapping high performance rnns to in memory neuromorphic chips"):
  with members:
    - "an ultra low power sigma delta neuron circuit"
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "overcoming the vanishing gradient problem in plain recurrent networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-3 (induced by source paper "processing data where it makes sense enabling in memory computation"):
  with members:
    - "architectural techniques for improving nand flash memory reliability"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "techniques for shared resource management in systems with throughput processors"
    - "errors in flash memory based solid state drives analysis mitigation and recovery"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "using ecc dram to adaptively increase memory capacity"
    - "lazypim efficient support for cache coherence in processing in memory architectures"

Connected through hyperedge HE-4 (induced by source paper "a neuromorphic boost to rnns using low pass filters"):
  with members:
    - "an ultra low power sigma delta neuron circuit"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "overcoming the vanishing gradient problem in plain recurrent networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-5 (induced by source paper "a workload and programming ease driven perspective of processing in memory"):
  with members:
    - "processing data where it makes sense enabling in memory computation"
    - "in dram bulk bitwise execution engine"
    - "rowhammer a retrospective"
    - "accelerating bulk bit wise x n or operation in processing in dram platform"
    - "shouji a fast and efficient pre alignment filter for sequence alignment"
    - "memristive devices for computation in memory"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "the rowhammer problem and other issues we may face as memory becomes denser"

Connected through hyperedge HE-6 (induced by source paper "enabling practical processing in and near memory for data intensive computing"):
  with members:
    - "processing data where it makes sense enabling in memory computation"
    - "rowhammer a retrospective"
    - "shouji a fast and efficient pre alignment filter for sequence alignment"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "using ecc dram to adaptively increase memory capacity"
    - "understanding and improving the latency of dram based memory systems"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"

Connected through hyperedge HE-7 (induced by source paper "near memory computing past present and future"):
  with members:
    - "memory and parallelism analysis using a platform independent approach"
    - "platform independent software analysis for near memory computing"
    - "identifying the potential of near data computing for apache spark"
    - "near memory address translation"
    - "performance characterization of in memory data analytics on a modern cloud server"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-8 (induced by source paper "enhancing programmability portability and performance with rich cross layer abstractions"):
  with members:
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "a framework for accelerating bottlenecks in gpu execution with assist warps"
    - "tensorflow a system for large scale machine learning"
    - "kernelet high throughput gpu kernel executions with dynamic slicing and scheduling"

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.AR(Hardware Architecture)