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: "scale sim systolic cnn accelerator simulator"
  Abstract: Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications. However, the research community lacks tools to insights on both the design trade-offs and efficient mapping strategies for systolic-array based accelerators. We introduce Systolic CNN Accelerator Simulator (SCALE-Sim), which is a configurable systolic array based cycle accurate DNN accelerator simulator. SCALE-Sim exposes various micro-architectural features as well as system integration parameters to the designer to enable comprehensive design space exploration. This is the first systolic-array simulator tuned for running DNNs to the best of our knowledge. Using SCALE-Sim, we conduct a suite of case studies and demonstrate the effect of bandwidth, data flow and aspect ratio on the overall runtime and energy of Deep Learning kernels across vision, speech, text, and games. We believe that these insights will be highly beneficial to architects and ML practitioners.

Connected through hyperedge HE-1 (induced by source paper "dnn chip predictor an analytical performance predictor for dnn accelerators with various dataflows and hardware architectures"):
  with members:
    - "skynet a champion model for dac sdc on low power object detection"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "dnn dataflow choice is overrated"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "eie efficient inference engine on compressed deep neural network"
    - "the microsoft 2016 conversational speech recognition system"

Connected through hyperedge HE-2 (induced by source paper "ecnn a block based and highly parallel cnn accelerator for edge inference"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "in datacenter performance analysis of a tensor processing unit"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "scnn an accelerator for compressed sparse convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "hardnet a low memory traffic network"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sparsely aggregated convolutional networks"
    - "single shot refinement neural network for object detection"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-4 (induced by source paper "prema a predictive multi task scheduling algorithm for preemptible neural processing units"):
  with members:
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "beyond human level accuracy computational challenges in deep learning"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "massive exploration of neural machine translation architectures"

Connected through hyperedge HE-5 (induced by source paper "asv accelerated stereo vision system"):
  with members:
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "dnn dataflow choice is overrated"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "eva 2 exploiting temporal redundancy in live computer vision"

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.DC(Distributed, Parallel, and Cluster Computing)