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: "mutual exclusivity loss for semi supervised deep learning"
  Abstract: In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive and effectively guides the decision boundary to lie on the low density space between the manifolds corresponding to different classes of data. Our proposed approach is general and can be used with any backpropagation-based learning method. We show through different experiments that our method can improve the object recognition performance of ConvNets using unlabeled data.

Connected through hyperedge HE-1 (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "label propagation for deep semi supervised learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-2 (induced by source paper "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"):
  with members:
    - "utilizing edge features in graph neural networks via variational information maximization"
    - "pre training graph neural networks"
    - "fast graph representation learning with pytorch geometric"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "semi supervised semantic segmentation needs strong high dimensional perturbations"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "s4 net geometry consistent semi supervised semantic segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "self ensembling for visual domain adaptation"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "label propagation for deep semi supervised learning"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mining on manifolds metric learning without labels"
    - "transductive label augmentation for improved deep network learning"
    - "deep co training for semi supervised image recognition"
    - "unsupervised representation learning by predicting image rotations"
    - "dynamic few shot visual learning without forgetting"
    - "low shot learning with large scale diffusion"
    - "transitive invariance for self supervised visual representation learning"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning using adversarial training with good and bad samples"):
  with members:
    - "semi supervised learning based on generative adversarial network a comparison between good gan and bad gan approach"
    - "high fidelity image generation with fewer labels"
    - "spectral normalization for generative adversarial networks"
    - "semi supervised learning with gans revisiting manifold regularization"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi supervised rare disease detection using generative adversarial network"
    - "triangle generative adversarial networks"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-6 (induced by source paper "semi supervised learning based on generative adversarial network a comparison between good gan and bad gan approach"):
  with members:
    - "semi supervised learning with gans revisiting manifold regularization"
    - "semi supervised rare disease detection using generative adversarial network"
    - "triangle generative adversarial networks"
    - "good semi supervised learning that requires a bad gan"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "triple generative adversarial nets"
    - "improved techniques for training gans"

Connected through hyperedge HE-7 (induced by source paper "saas speed as a supervisor for semi supervised learning"):
  with members:
    - "graph partition neural networks for semi supervised classification"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "regularizing neural networks by penalizing confident output distributions"

Connected through hyperedge HE-8 (induced by source paper "learning by association a versatile semi supervised training method for neural networks"):
  with members:
    - "improved techniques for training gans"
    - "multi task cross lingual sequence tagging from scratch"
    - "domain separation networks"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of video representations using lstms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

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)