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: "link and code fast indexing with graphs and compact regression codes"
  Abstract: Similarity search approaches based on graph walks have recently attained outstanding speed-accuracy trade-offs, taking aside the memory requirements. In this paper, we revisit these approaches by considering, additionally, the memory constraint required to index billions of images on a single server. This leads us to propose a method based both on graph traversal and compact representations. We encode the indexed vectors using quantization and exploit the graph structure to refine the similarity estimation. #R##N#In essence, our method takes the best of these two worlds: the search strategy is based on nested graphs, thereby providing high precision with a relatively small set of comparisons. At the same time it offers a significant memory compression. As a result, our approach outperforms the state of the art on operating points considering 64-128 bytes per vector, as demonstrated by our results on two billion-scale public benchmarks.

Connected through hyperedge HE-1 (induced by source paper "fast compact and highly scalable visual place recognition through sequence based matching of overloaded representations"):
  with members:
    - "a hybrid compact neural architecture for visual place recognition"
    - "btel a binary tree encoding approach for visual localization"
    - "lost appearance invariant place recognition for opposite viewpoints using visual semantics"
    - "hbst a hamming distance embedding binary search tree for feature based visual place recognition"
    - "learning to index for nearest neighbor search"
    - "billion scale similarity search with gpus"
    - "x view graph based semantic multi view localization"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-2 (induced by source paper "unsupervised rank preserving hashing for large scale image retrieval"):
  with members:
    - "polysemous codes"
    - "deep binary reconstruction for cross modal hashing"
    - "patternnet visual pattern mining with deep neural network"
    - "a survey on learning to hash"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "how should we evaluate supervised hashing"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "in defense of product quantization"

Connected through hyperedge HE-4 (induced by source paper "pyramid a general framework for distributed similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "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-5 (induced by source paper "norm explicit quantization improving vector quantization for maximum inner product search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "billion scale similarity search with gpus"
    - "clustering is efficient for approximate maximum inner product search"
    - "quantization based fast inner product search"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

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

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)