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: "fast supervised discrete hashing"
  Abstract: Learning-based hashing algorithms are ``hot topics" because they can greatly increase the scale at which existing methods operate. In this paper, we propose a new learning-based hashing method called ``fast supervised discrete hashing" (FSDH) based on ``supervised discrete hashing" (SDH). Regressing the training examples (or hash code) to the corresponding class labels is widely used in ordinary least squares regression. Rather than adopting this method, FSDH uses a very simple yet effective regression of the class labels of training examples to the corresponding hash code to accelerate the algorithm. To the best of our knowledge, this strategy has not previously been used for hashing. Traditional SDH decomposes the optimization into three sub-problems, with the most critical sub-problem - discrete optimization for binary hash codes - solved using iterative discrete cyclic coordinate descent (DCC), which is time-consuming. However, FSDH has a closed-form solution and only requires a single rather than iterative hash code-solving step, which is highly efficient. Furthermore, FSDH is usually faster than SDH for solving the projection matrix for least squares regression, making FSDH generally faster than SDH. For example, our results show that FSDH is about 12-times faster than SDH when the number of hashing bits is 128 on the CIFAR-10 data base, and FSDH is about 151-times faster than FastHash when the number of hashing bits is 64 on the MNIST data-base. Our experimental results show that FSDH is not only fast, but also outperforms other comparative methods.

Connected through hyperedge HE-1 (induced by source paper "asymmetric correlation quantization hashing for cross modal retrieval"):
  with members:
    - "collaborative quantization for cross modal similarity search"
    - "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 "simultaneous region localization and hash coding for fine grained image retrieval"):
  with members:
    - "deep policy hashing network with listwise supervision"
    - "deep triplet quantization"
    - "feature pyramid hashing"
    - "ssdh semi supervised deep hashing for large scale image retrieval"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep semantic hashing with generative adversarial networks"
    - "learning to navigate for fine grained classification"
    - "deep saliency hashing"

Connected through hyperedge HE-3 (induced by source paper "distillhash unsupervised deep hashing by distilling data pairs"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "triplet based deep hashing network for cross modal retrieval"
    - "coupled cyclegan unsupervised hashing network for cross modal retrieval"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "zero shot sketch image hashing"
    - "evolutionary generative adversarial networks"
    - "masking a new perspective of noisy supervision"
    - "self supervised adversarial hashing networks for cross modal retrieval"

Connected through hyperedge HE-4 (induced by source paper "deep policy hashing network with listwise supervision"):
  with members:
    - "supervised learning of semantics preserving hash via deep convolutional neural networks"
    - "deep reinforcement learning for image hashing"
    - "blockdrop dynamic inference paths in residual networks"
    - "hashnet deep learning to hash by continuation"
    - "fast training of triplet based deep binary embedding networks"
    - "a survey on learning to hash"
    - "asynchronous methods for deep reinforcement learning"
    - "simultaneous feature learning and hash coding with deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "shared predictive cross modal deep quantization"):
  with members:
    - "query adaptive hash code ranking for large scale multi view visual search"
    - "supervised discrete hashing with relaxation"
    - "collaborative quantization for cross modal similarity search"
    - "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-6 (induced by source paper "push for quantization deep fisher hashing"):
  with members:
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "deep cross modal hashing"
    - "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"
    - "optimizing affinity based binary hashing using auxiliary coordinates"

Connected through hyperedge HE-7 (induced by source paper "sadih semantic aware discrete hashing"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "highly economized multi view binary compression for scalable image clustering"
    - "a survey on learning to hash"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "very deep convolutional networks for large scale image recognition"
    - "revisiting kernelized locality sensitive hashing for improved large scale image retrieval"
    - "fast supervised hashing with decision trees for high dimensional data"

Connected through hyperedge HE-8 (induced by source paper "hadamard matrix guided online hashing"):
  with members:
    - "shared predictive cross modal deep quantization"
    - "towards optimal discrete online hashing with balanced similarity"
    - "a survey on learning to hash"
    - "very deep convolutional networks for large scale image recognition"
    - "simple and deterministic matrix sketching"

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.LG(Machine Learning)