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: "communication optimal convolutional neural nets"
  Abstract: Efficiently executing convolutional neural nets (CNNs) is important in many machine-learning tasks. Since the cost of moving a word of data, either between levels of a memory hierarchy or between processors over a network, is much higher than the cost of an arithmetic operation, minimizing data movement is critical to performance optimization. In this paper, we present both new lower bounds on data movement needed for CNNs, and optimal sequential algorithms that attain these lower bounds. In most common cases, our optimal algorithms can attain significantly more data reuse than matrix multiplication.

Connected through hyperedge HE-1 (induced by source paper "flexible communication avoiding matrix multiplication on fpga with high level synthesis"):
  with members:
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "hlslib software engineering for hardware design"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "integrated model batch and domain parallelism in training neural networks"
    - "communication lower bounds for distributed memory computations"
    - "communication lower bounds for distributed memory computations"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-2 (induced by source paper "improving strong scaling of cnn training by exploiting finer grained parallelism"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "exploring the limits of weakly supervised pretraining"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "measuring the effects of data parallelism on neural network training"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "imagenet training in minutes"
    - "an analysis of deep neural network models for practical applications"
    - "integrated model batch and domain parallelism in training neural networks"

Connected through hyperedge HE-3 (induced by source paper "anatomy of high performance deep learning convolutions on simd architectures"):
  with members:
    - "mixed precision training of convolutional neural networks using integer operations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "on scale out deep learning training for cloud and hpc"
    - "mixed precision training"
    - "low memory gemm based convolution algorithms for deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "parallel multi channel convolution using general matrix multiplication"
    - "distributed deep learning using synchronous stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "mesh tensorflow deep learning for supercomputers"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "integrated model batch and domain parallelism in training neural networks"
    - "attention is all you need"
    - "exploring the limits of language modeling"
    - "scalable task based algorithm for multiplication of block rank sparse matrices"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-5 (induced by source paper "isa mapper a compute and hardware agnostic deep learning compiler"):
  with members:
    - "tvm end to end optimization stack for deep learning"
    - "anatomy of high performance deep learning convolutions on simd architectures"
    - "learning to optimize tensor programs"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "rigid motion scattering for texture classification"

Connected through hyperedge HE-6 (induced by source paper "a tight i o lower bound for matrix multiplication"):
  with members:
    - "a lower bound technique for communication in bsp"
    - "communication lower bounds for matricized tensor times khatri rao product"
    - "communication lower bounds of bilinear algorithms for symmetric tensor contractions"
    - "communication lower bounds for distributed memory computations"
    - "communication lower bounds for distributed memory computations"
    - "minimizing communication in linear algebra"
    - "revisiting matrix product on master worker platforms"

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.DS(Data Structures and Algorithms)