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: "bolt accelerated data mining with fast vector compression"
  Abstract: Vectors of data are at the heart of machine learning and data mining. Recently, vector quantization methods have shown great promise in reducing both the time and space costs of operating on vectors. We introduce a vector quantization algorithm that can compress vectors over 12x faster than existing techniques while also accelerating approximate vector operations such as distance and dot product computations by up to 10x. Because it can encode over 2GB of vectors per second, it makes vector quantization cheap enough to employ in many more circumstances. For example, using our technique to compute approximate dot products in a nested loop can multiply matrices faster than a state-of-the-art BLAS implementation, even when our algorithm must first compress the matrices. In addition to showing the above speedups, we demonstrate that our approach can accelerate nearest neighbor search and maximum inner product search by over 100x compared to floating point operations and 10x compared to other vector quantization methods. Our approximate Euclidean distance and dot product computations are not only faster than those of related algorithms with slower encodings, but also faster than Hamming distance computations, which have direct hardware support on the tested platforms. We also assess the errors of our algorithm's approximate distances and dot products, and find that it is competitive with existing, slower vector quantization algorithms.

Connected through hyperedge HE-1 (induced by source paper "the space complexity of inner product filters"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "approximate nearest neighbors in limited space"
    - "adaptive mapreduce similarity joins"
    - "practical data dependent metric compression with provable guarantees"
    - "distributed pcp theorems for hardness of approximation in p"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "scalable and robust set similarity join"
    - "on the complexity of inner product similarity join"

Connected through hyperedge HE-2 (induced by source paper "quicker adc unlocking the hidden potential of product quantization with simd"):
  with members:
    - "derived codebooks for high accuracy nearest neighbor search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "faster population counts using avx2 instructions"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "subic a supervised structured binary code for image search"
    - "billion scale similarity search with gpus"
    - "cuckoo hash tables high performance hash tables for networking applications"

Connected through hyperedge HE-3 (induced by source paper "efficient inner product approximation in hybrid spaces"):
  with members:
    - "deep cross network for ad click predictions"
    - "accelerated nearest neighbor search with quick adc"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "practical and optimal lsh for angular distance"
    - "quantization based fast inner product search"
    - "distributed representations of sentences and documents"
    - "stacked quantizers for compositional vector compression"

Connected through hyperedge HE-4 (induced by source paper "local orthogonal decomposition for maximum inner product search"):
  with members:
    - "billion scale similarity search with gpus"
    - "learning spread out local feature descriptors"
    - "a greedy approach for budgeted maximum inner product search"
    - "stacked quantizers for compositional vector compression"

Connected through hyperedge HE-5 (induced by source paper "reconfigurable inverted index"):
  with members:
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "pqk means billion scale clustering for product quantized codes"
    - "accelerated nearest neighbor search with quick adc"
    - "efficient large scale approximate nearest neighbor search on the gpu"

Connected through hyperedge HE-6 (induced by source paper "learning to index for nearest neighbor search"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "online product quantization"
    - "pqk means billion scale clustering for product quantized codes"
    - "accelerated nearest neighbor search with quick adc"
    - "a survey on learning to hash"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"
    - "hashing for similarity search a survey"

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.PF(Performance)