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: "high performance monte carlo simulation of ising model on tpu clusters"
  Abstract: Large scale deep neural networks profited from an emerging class of AI accelerators. Although the accelerators are specialized for machine learning, some of their designs are general enough for other computing intensive applications. Cloud TPU, as one of them, offers tremendous computing resources and is easily accessible through TensorFlow by expressing the computation in a graph. In this paper, we leverage this powerful hardware combined with the expressiveness of TensorFlow to simulate the Ising model on a $2$-dimensional lattice. We modify the computationally intensive part of the checkerboard algorithm into matrix operations to exploit Cloud TPU's highly efficient matrix unit. In our experiments, we demonstrate that our implementation outperforms the best published benchmarks to our knowledge by 60% in single core and 250% in multiple cores with linear scaling. We also show the performance improvement of using low precision arithmetic---bfloat16 instead of float32---without sacrificing any accuracy.

Connected through hyperedge HE-1 (induced by source paper "large scale discrete fourier transform on tpus"):
  with members:
    - "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "a flexible framework for parallel multi dimensional dfts"
    - "tensor processing units for financial monte carlo"
    - "in datacenter performance analysis of a tensor processing unit"
    - "a berkeley view of systems challenges for ai"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "p3dfft a framework for parallel computations of fourier transforms in three dimensions"

Connected through hyperedge HE-2 (induced by source paper "a study of bfloat16 for deep learning training"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "automatic full compilation of julia programs and ml models to cloud tpus"
    - "deep cross network for ad click predictions"
    - "mixed precision training"
    - "ternary neural networks with fine grained quantization"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "high resolution imaging on tpus"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "inundation modeling in data scarce regions"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "xception deep learning with depthwise separable convolutions"

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)