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: "gpgpu performance estimation with core and memory frequency scaling"
  Abstract: Graphics Processing Units (GPUs) support dynamic voltage and frequency scaling (DVFS) in order to balance computational performance and energy consumption. However, there still lacks simple and accurate performance estimation of a given GPU kernel under different frequency settings on real hardware, which is important to decide best frequency configuration for energy saving. This paper reveals a fine-grained model to estimate the execution time of GPU kernels with both core and memory frequency scaling. Over a 2.5x range of both core and memory frequencies among 12 GPU kernels, our model achieves accurate results (within 3.5\%) on real hardware. Compared with the cycle-level simulators, our model only needs some simple micro-benchmark to extract a set of hardware parameters and performance counters of the kernels to produce this high accuracy.

Connected through hyperedge HE-1 (induced by source paper "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"):
  with members:
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "performance and power evaluation of ai accelerators for training deep learning models"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "benchmarking the performance and power of ai accelerators for ai training"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "attention is all you need"
    - "mixed precision training"

Connected through hyperedge HE-4 (induced by source paper "delta gpu performance model for deep learning applications with in depth memory system traffic analysis"):
  with members:
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "optimizing memory efficiency for deep convolutional neural networks on gpus"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "dissecting gpu memory hierarchy through microbenchmarking"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

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.PF(Performance)