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: "exploration of pattern matching techniques for lossy compression on cosmology simulation data sets"
  Abstract: Because of the vast volume of data being produced by today's scientific simulations, lossy compression allowing user-controlled information loss can significantly reduce the data size and the I/O burden. However, for large-scale cosmology simulation, such as the Hardware/Hybrid Accelerated Cosmology Code (HACC), where memory overhead constraints restrict compression to only one snapshot at a time, the lossy compression ratio is extremely limited because of the fairly low spatial coherence and high irregularity of the data. In this work, we propose a pattern-matching (similarity searching) technique to optimize the prediction accuracy and compression ratio of SZ lossy compressor on the HACC data sets. We evaluate our proposed method with different configurations and compare it with state-of-the-art lossy compressors. Experiments show that our proposed optimization approach can improve the prediction accuracy and reduce the compressed size of quantization codes compared with SZ. We present several lessons useful for future research involving pattern-matching techniques for lossy compression.

Connected through hyperedge HE-1 (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"
    - "significantly improving lossy compression for scientific data sets based on multidimensional prediction and error controlled quantization"
    - "on the scalability of data reduction techniques in current and upcoming hpc systems from an application perspective"
    - "in depth exploration of single snapshot lossy compression techniques for n body simulations"

Connected through hyperedge HE-2 (induced by source paper "fixed psnr lossy compression for scientific data"):
  with members:
    - "significantly improving lossy compression for scientific data sets based on multidimensional prediction and error controlled quantization"
    - "in depth exploration of single snapshot lossy compression techniques for n body simulations"

Connected through hyperedge HE-3 (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"
    - "significantly improving lossy compression for scientific data sets based on multidimensional prediction and error controlled quantization"

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)