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: "scalable image retrieval by sparse product quantization"
  Abstract: Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "generalized product quantization network for semi supervised image retrieval"):
  with members:
    - "end to end supervised product quantization for image search and retrieval"
    - "deep triplet quantization"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "a closer look at few shot classification"
    - "open set domain adaptation by backpropagation"
    - "subic a supervised structured binary code for image search"
    - "prototypical networks for few shot learning"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-2 (induced by source paper "transductive zero shot hashing for multi label image retrieval"):
  with members:
    - "exploring auxiliary context discrete semantic transfer hashing for scalable image retrieval"
    - "supervised discrete hashing with relaxation"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "deep hashing network for unsupervised domain adaptation"
    - "hashing in the zero shot framework with domain adaptation"
    - "hashnet deep learning to hash by continuation"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-3 (induced by source paper "improved deep hashing with soft pairwise similarity for multi label image retrieval"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "instance aware hashing for multi label image retrieval"
    - "compact hash codes for efficient visual descriptors retrieval in large scale databases"
    - "robust gait recognition by integrating inertial and rgbd sensors"

Connected through hyperedge HE-4 (induced by source paper "pqtable non exhaustive fast search for product quantized codes using hash tables"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "billion scale similarity search with gpus"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "approximate search with quantized sparse representations"
    - "learning to hash for indexing big data a survey"
    - "optimized cartesian k means"
    - "going deeper with convolutions"
    - "improving bilayer product quantization for billion scale approximate nearest neighbors in high dimensions"

Connected through hyperedge HE-5 (induced by source paper "indexing of cnn features for large scale image search"):
  with members:
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "hcp a flexible cnn framework for multi label image classification"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"
    - "efficient training of very deep neural networks for supervised hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "deep residual learning for image recognition"

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)