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: "supervised discrete hashing with relaxation"
  Abstract: Data-dependent hashing has recently attracted attention due to being able to support efficient retrieval and storage of high-dimensional data such as documents, images, and videos. In this paper, we propose a novel learning-based hashing method called "Supervised Discrete Hashing with Relaxation" (SDHR) based on "Supervised Discrete Hashing" (SDH). SDH uses ordinary least squares regression and traditional zero-one matrix encoding of class label information as the regression target (code words), thus fixing the regression target. In SDHR, the regression target is instead optimized. The optimized regression target matrix satisfies a large margin constraint for correct classification of each example. Compared with SDH, which uses the traditional zero-one matrix, SDHR utilizes the learned regression target matrix and, therefore, more accurately measures the classification error of the regression model and is more flexible. As expected, SDHR generally outperforms SDH. Experimental results on two large-scale image datasets (CIFAR-10 and MNIST) and a large-scale and challenging face dataset (FRGC) demonstrate the effectiveness and efficiency of SDHR.

Connected through hyperedge HE-1 (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"
    - "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"
    - "scalable image retrieval by sparse product quantization"
    - "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-2 (induced by source paper "deep hashing learning for visual and semantic retrieval of remote sensing images"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "exploiting deep features for remote sensing image retrieval a systematic investigation"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "sadih semantic aware discrete hashing"):
  with members:
    - "fast supervised discrete hashing"
    - "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-5 (induced by source paper "fast supervised discrete hashing"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "supervised hashing using graph cuts and boosted decision trees"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "inductive hashing on manifolds"

Connected through hyperedge HE-6 (induced by source paper "improved deep hashing with soft pairwise similarity for multi label image retrieval"):
  with members:
    - "deep supervised discrete hashing"
    - "hashnet deep learning to hash by continuation"
    - "deep supervised hashing with triplet labels"
    - "scalable image retrieval by sparse product quantization"
    - "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"

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)