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: "collaborative quantization for cross modal similarity search"
  Abstract: Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in the single-modal similarity search. We propose a cross-modal quantization approach, which is among the early attempts to introduce quantization into cross-modal search. The major contribution lies in jointly learning the quantizers for both modalities through aligning the quantized representations for each pair of image and text belonging to a document. In addition, our approach simultaneously learns the common space for both modalities in which quantization is conducted to enable efficient and effective search using the Euclidean distance computed in the common space with fast distance table lookup. Experimental results compared with several competitive algorithms over three benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "asymmetric correlation quantization hashing for cross modal retrieval"):
  with members:
    - "fast supervised discrete hashing"
    - "deep binary reconstruction for cross modal hashing"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "stacked quantizers for compositional vector compression"
    - "multimodal similarity preserving hashing"

Connected through hyperedge HE-2 (induced by source paper "shared predictive cross modal deep quantization"):
  with members:
    - "query adaptive hash code ranking for large scale multi view visual search"
    - "fast supervised discrete hashing"
    - "supervised discrete hashing with relaxation"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "composite correlation quantization for efficient multimodal retrieval"
    - "deep cross modal hashing"
    - "domain separation networks"

Connected through hyperedge HE-3 (induced by source paper "deep cross modal hashing with hashing functions and unified hash codes jointly learning"):
  with members:
    - "deep semantic hashing with generative adversarial networks"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised triplet hashing for fast image retrieval"
    - "discrete latent factor model for cross modal hashing"
    - "asymmetric deep supervised hashing"
    - "hashnet deep learning to hash by continuation"
    - "supervised deep hashing for hierarchical labeled data"
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"

Connected through hyperedge HE-4 (induced by source paper "coupled cyclegan unsupervised hashing network for cross modal retrieval"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "unsupervised generative adversarial cross modal hashing"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep cross modal hashing"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "self supervised adversarial hashing networks for cross modal retrieval"):
  with members:
    - "deep binaries encoding semantic rich cues for efficient textual visual cross retrieval"
    - "deep cross modal hashing"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "a survey on multi view learning"
    - "representation learning a review and new perspectives"
    - "multimodal similarity preserving hashing"

Connected through hyperedge HE-6 (induced by source paper "pqtable non exhaustive fast search for product quantized codes using hash tables"):
  with members:
    - "billion scale similarity search with gpus"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "scalable image retrieval by sparse product quantization"
    - "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-7 (induced by source paper "a survey on learning to hash"):
  with members:
    - "scalable discrete supervised hash learning with asymmetric matrix factorization"
    - "approximate search with quantized sparse representations"
    - "binary hashing with semidefinite relaxation and augmented lagrangian"
    - "learning to hash with binary deep neural network"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "hashing with binary autoencoders"
    - "learning to hash for indexing big data 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)