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: "decoding billions of integers per second through vectorization"
  Abstract: In many important applications -- such as search engines and relational database systems -- data is stored in the form of arrays of integers. Encoding and, most importantly, decoding of these arrays consumes considerable CPU time. Therefore, substantial effort has been made to reduce costs associated with compression and decompression. In particular, researchers have exploited the superscalar nature of modern processors and SIMD instructions. Nevertheless, we introduce a novel vectorized scheme called SIMD-BP128 that improves over previously proposed vectorized approaches. It is nearly twice as fast as the previously fastest schemes on desktop processors (varint-G8IU and PFOR). At the same time, SIMD-BP128 saves up to 2 bits per integer. For even better compression, we propose another new vectorized scheme (SIMD-FastPFOR) that has a compression ratio within 10% of a state-of-the-art scheme (Simple-8b) while being two times faster during decoding.

Connected through hyperedge HE-1 (induced by source paper "permute to train a new dimension to training deep neural networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "weight agnostic neural networks"
    - "what s hidden in a randomly weighted neural network"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "tuple oriented compression for large scale mini batch stochastic gradient descent"):
  with members:
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "a cost based optimizer for gradient descent optimization"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "towards linear algebra over normalized data"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-3 (induced by source paper "techniques for inverted index compression"):
  with members:
    - "on optimally partitioning variable byte codes"
    - "on slicing sorted integer sequences"
    - "stream vbyte faster byte oriented integer compression"
    - "roaring bitmaps implementation of an optimized software library"
    - "compressing graphs and indexes with recursive graph bisection"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"

Connected through hyperedge HE-4 (induced by source paper "on slicing sorted integer sequences"):
  with members:
    - "on optimally partitioning variable byte codes"
    - "stream vbyte faster byte oriented integer compression"
    - "roaring bitmaps implementation of an optimized software library"
    - "a fast x86 implementation of select"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "vectorized vbyte decoding"
    - "quasi succinct indices"

Connected through hyperedge HE-5 (induced by source paper "learning multi stage sparsification for maximum clique enumeration"):
  with members:
    - "fine grained search space classification for hard enumeration variants of subset problems"
    - "neural combinatorial optimization with reinforcement learning"
    - "simd compression and the intersection of sorted integers"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-6 (induced by source paper "beyond the inverted index"):
  with members:
    - "diffnodesets an efficient structure for fast mining frequent itemsets"

Connected through hyperedge HE-7 (induced by source paper "variable byte encoding is now space efficient too"):
  with members:
    - "stream vbyte faster byte oriented integer compression"
    - "universal indexes for highly repetitive document collections"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "quasi succinct indices"
    - "on optimally partitioning a text to improve its compression"
    - "improving table compression with combinatorial optimization"

Connected through hyperedge HE-8 (induced by source paper "sprintz time series compression for the internet of things"):
  with members:
    - "a general simd based approach to accelerating compression algorithms"
    - "compressed sensing of multi channel eeg signals the simultaneous cosparsity and low rank optimization"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "a better alternative to piecewise linear time series segmentation"

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.IR(Information Retrieval)