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: "significantly improving lossy compression for scientific data sets based on multidimensional prediction and error controlled quantization"
  Abstract: Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we design a new error-controlled lossy compression algorithm for large-scale scientific data. Our key contribution is significantly improving the prediction hitting rate (or prediction accuracy) for each data point based on its nearby data values along multiple dimensions. We derive a series of multilayer prediction formulas and their unified formula in the context of data compression. One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn. We explore the best layer for the prediction by considering the impact of compression errors on the prediction accuracy. Moreover, we propose an adaptive error-controlled quantization encoder, which can further improve the prediction hitting rate considerably. The data size can be reduced significantly after performing the variable-length encoding because of the uneven distribution produced by our quantization encoder. We evaluate the new compressor on production scientific data sets and compare it with many other state-of-the-art compressors: GZIP, FPZIP, ZFP, SZ-1.1, and ISABELA. Experiments show that our compressor is the best in class, especially with regard to compression factors (or bit-rates) and compression errors (including RMSE, NRMSE, and PSNR). Our solution is better than the second-best solution by more than a 2x increase in the compression factor and 3.8x reduction in the normalized root mean squared error on average, with reasonable error bounds and user-desired bit-rates.

Connected through hyperedge HE-1 (induced by source paper "fraz a generic high fidelity fixed ratio lossy compression framework for scientific floating point data"):
  with members:
    - "optimizing lossy compression rate distortion from automatic online selection between sz and zfp"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "deepsz a novel framework to compress deep neural networks by using error bounded lossy compression"):
  with members:
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "optimizing lossy compression rate distortion from automatic online selection between sz and zfp"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "in depth exploration of single snapshot lossy compression techniques for n body simulations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-3 (induced by source paper "z checker a framework for assessing lossy compression of scientific data"):
  with members:
    - "parallel tensor compression for large scale scientific data"
    - "universal numerical encoder and profiler reduces computing s memory wall with software fpga and soc implementations"
    - "the earth system grid supporting the next generation of climate modeling research"

Connected through hyperedge HE-4 (induced by source paper "optimizing lossy compression rate distortion from automatic online selection between sz and zfp"):
  with members:
    - "z checker a framework for assessing lossy compression of scientific data"
    - "on the scalability of data reduction techniques in current and upcoming hpc systems from an application perspective"
    - "exploration of pattern matching techniques for lossy compression on cosmology simulation data sets"
    - "in depth exploration of single snapshot lossy compression techniques for n body simulations"

Connected through hyperedge HE-5 (induced by source paper "fixed psnr lossy compression for scientific data"):
  with members:
    - "exploration of pattern matching techniques for lossy compression on cosmology simulation data sets"
    - "in depth exploration of single snapshot lossy compression techniques for n body simulations"

Connected through hyperedge HE-6 (induced by source paper "improving performance of iterative methods by lossy checkponting"):
  with members:
    - "parallel tensor compression for large scale scientific data"
    - "evaluating the impact of sdc on the gmres iterative solver"

Connected through hyperedge HE-7 (induced by source paper "on the scalability of data reduction techniques in current and upcoming hpc systems from an application perspective"):
  with members:
    - "alpaka an abstraction library for parallel kernel acceleration"
    - "performance portable many core plasma simulations porting picongpu to openpower and beyond"
    - "in situ steerable hardware independent and data structure agnostic visualization with isaac"

Connected through hyperedge HE-8 (induced by source paper "in depth exploration of single snapshot lossy compression techniques for n body simulations"):
  with members:
    - "z checker a framework for assessing lossy compression of scientific data"
    - "exploration of pattern matching techniques for lossy compression on cosmology simulation data sets"

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.IT(Information Theory)