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: "accelerated nearest neighbor search with quick adc"
  Abstract: Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. Because it offers low responses times, Product Quantization (PQ) is a popular solution. PQ compresses high-dimensional vectors into short codes using several sub-quantizers, which enables in-RAM storage of large databases. This allows fast answers to NN queries, without accessing the SSD or HDD. The key feature of PQ is that it can compute distances between short codes and high-dimensional vectors using cache-resident lookup tables. The efficiency of this technique, named Asymmetric Distance Computation (ADC), remains limited because it performs many cache accesses.   In this paper, we introduce Quick ADC, a novel technique that achieves a 3 to 6 times speedup over ADC by exploiting Single Instruction Multiple Data (SIMD) units available in current CPUs. Efficiently exploiting SIMD requires algorithmic changes to the ADC procedure. Namely, Quick ADC relies on two key modifications of ADC: (i) the use 4-bit sub-quantizers instead of the standard 8-bit sub-quantizers and (ii) the quantization of floating-point distances. This allows Quick ADC to exceed the performance of state-of-the-art systems, e.g., it achieves a Recall@100 of 0.94 in 3.4 ms on 1 billion SIFT descriptors (128-bit codes).

Connected through hyperedge HE-1 (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-2 (induced by source paper "distributed and consistent multi image feature matching via quickmatch"):
  with members:
    - "ps dbscan an efficient parallel dbscan algorithm based on platform of ai pai"
    - "billion scale similarity search with gpus"
    - "anchornet a weakly supervised network to learn geometry sensitive features for semantic matching"
    - "multi image semantic matching by mining consistent features"
    - "distributable consistent multi object matching"
    - "multi image matching via fast alternating minimization"
    - "near optimal joint object matching via convex relaxation"
    - "hypercolumns for object segmentation and fine grained localization"

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"
    - "bolt accelerated data mining with fast vector compression"
    - "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 "derived codebooks for high accuracy nearest neighbor search"):
  with members:
    - "quicker adc unlocking the hidden potential of product quantization with simd"
    - "polysemous codes"
    - "searching in one billion vectors re rank with source coding"

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"
    - "bolt accelerated data mining with fast vector compression"
    - "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"
    - "bolt accelerated data mining with fast vector compression"
    - "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.CV(Computer Vision and Pattern Recognition)