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: "in defense of product quantization"
  Abstract: Despite their widespread adoption, Product Quantization techniques were recently shown to be inferior to other hashing techniques. In this work, we present an improved Deep Product Quantization (DPQ) technique that leads to more accurate retrieval and classification than the latest state of the art methods, while having similar computational complexity and memory footprint as the Product Quantization method. To our knowledge, this is the first work to introduce a representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal. DPQ explicitly learns soft and hard representations to enable an efficient and accurate asymmetric search, by using a straight-through estimator. A novel loss function, Joint Central Loss, is introduced, which both improves the retrieval performance, and decreases the discrepancy between the soft and the hard representations. Finally, by using a normalization technique, we improve the results for cross-domain category retrieval.

Connected through hyperedge HE-1 (induced by source paper "unsupervised neural quantization for compressed domain similarity search"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "subic a supervised structured binary code for image search"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "super convergence very fast training of residual networks using large learning rates"

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 "beyond product quantization deep progressive quantization for image retrieval"):
  with members:
    - "a survey on learning to hash"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "stacked quantizers for compositional vector compression"

Connected through hyperedge HE-4 (induced by source paper "interleaved composite quantization for high dimensional similarity search"):
  with members:
    - "billion scale similarity search with gpus"
    - "how should we evaluate supervised hashing"

Connected through hyperedge HE-5 (induced by source paper "learning hash codes via hamming distance targets"):
  with members:
    - "deep hyperspherical learning"
    - "billion scale similarity search with gpus"
    - "riemannian approach to batch normalization"
    - "hashnet deep learning to hash by continuation"
    - "deep hashing with triplet quantization loss"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "a survey on learning to hash"

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)