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: "revisiting the inverted indices for billion scale approximate nearest neighbors"
  Abstract: This work addresses the problem of billion-scale nearest neighbor search. The state-of-the-art retrieval systems for billion-scale databases are currently based on the inverted multi-index, the recently proposed generalization of the inverted index structure. The multi-index provides a very fine-grained partition of the feature space that allows extracting concise and accurate short-lists of candidates for the search queries. In this paper, we argue that the potential of the simple inverted index was not fully exploited in previous works and advocate its usage both for the highly-entangled deep descriptors and relatively disentangled SIFT descriptors. We introduce a new retrieval system that is based on the inverted index and outperforms the multi-index by a large margin for the same memory consumption and construction complexity. For example, our system achieves the state-of-the-art recall rates up to six times faster on the dataset of one billion deep descriptors compared to the efficient implementation of the inverted multi-index from the FAISS library.

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"
    - "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"
    - "in defense of product quantization"

Connected through hyperedge HE-2 (induced by source paper "exquisitor interactive learning at large"):
  with members:
    - "cost effective active learning for deep image classification"
    - "approximate furthest neighbor with application to annulus query"
    - "active decision boundary annotation with deep generative models"
    - "a survey on learning to hash"
    - "yfcc100m the new data in multimedia research"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "pyramid a general framework for distributed similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "low shot learning with large scale diffusion"
    - "billion scale similarity search with gpus"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "a survey on learning to hash"
    - "clustering is efficient for approximate maximum inner product search"
    - "fast neighborhood graph search using cartesian concatenation"

Connected through hyperedge HE-4 (induced by source paper "vector and line quantization for billion scale similarity search on gpus"):
  with members:
    - "billion scale similarity search with gpus"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "searching in one billion vectors re rank with source coding"

Connected through hyperedge HE-5 (induced by source paper "exact and or fast nearest neighbors"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "fast k nearest neighbour search via prioritized dci"
    - "billion scale similarity search with gpus"
    - "key value memory networks for directly reading documents"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-6 (induced by source paper "reconfigurable inverted index"):
  with members:
    - "link and code fast indexing with graphs and compact regression codes"
    - "polysemous codes"
    - "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"
    - "bolt accelerated data mining with fast vector compression"
    - "efficient large scale approximate nearest neighbor search on the gpu"

Connected through hyperedge HE-7 (induced by source paper "learning to index for nearest neighbor search"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "online product quantization"
    - "pqk means billion scale clustering for product quantized codes"
    - "accelerated nearest neighbor search with quick adc"
    - "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)