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: "hybridnet classification and reconstruction cooperation for semi supervised learning"
  Abstract: In this paper, we introduce a new model for leveraging unlabeled data to improve generalization performances of image classifiers: a two-branch encoder-decoder architecture called HybridNet. The first branch receives supervision signal and is dedicated to the extraction of invariant class-related representations. The second branch is fully unsupervised and dedicated to model information discarded by the first branch to reconstruct input data. To further support the expected behavior of our model, we propose an original training objective. It favors stability in the discriminative branch and complementarity between the learned representations in the two branches. HybridNet is able to outperform state-of-the-art results on CIFAR-10, SVHN and STL-10 in various semi-supervised settings. In addition, visualizations and ablation studies validate our contributions and the behavior of the model on both CIFAR-10 and STL-10 datasets.

Connected through hyperedge HE-1 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction"):
  with members:
    - "differentiable physics informed graph networks"
    - "improved conditional vrnns for video prediction"
    - "neural jump stochastic differential equations"
    - "unsupervised learning of object structure and dynamics from videos"
    - "compositional video prediction"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"

Connected through hyperedge HE-2 (induced by source paper "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "gland instance segmentation using deep multichannel neural networks"
    - "semantic segmentation using adversarial networks"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "dualdis dual branch disentangling with adversarial learning"):
  with members:
    - "a two step disentanglement method"
    - "learning disentangled representations with reference based variational autoencoders"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "label propagation for deep semi supervised learning"
    - "dual swap disentangling"
    - "exploring disentangled feature representation beyond face identification"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"

Connected through hyperedge HE-4 (induced by source paper "adversarial feature distribution alignment for semi supervised learning"):
  with members:
    - "transferrable prototypical networks for unsupervised domain adaptation"
    - "saas speed as a supervisor for semi supervised learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "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"
    - "triple generative adversarial nets"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "repetitive reprediction deep decipher for semi supervised learning"):
  with members:
    - "label propagation for deep semi supervised learning"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "deep co training for semi supervised image recognition"
    - "deep label distribution learning with label ambiguity"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"

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)