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: "learning loss functions for semi supervised learning via discriminative adversarial networks"
  Abstract: We propose discriminative adversarial networks (DAN) for semi-supervised learning and loss function learning. Our DAN approach builds upon generative adversarial networks (GANs) and conditional GANs but includes the key differentiator of using two discriminators instead of a generator and a discriminator. DAN can be seen as a framework to learn loss functions for predictors that also implements semi-supervised learning in a straightforward manner. We propose instantiations of DAN for two different prediction tasks: classification and ranking. Our experimental results on three datasets of different tasks demonstrate that DAN is a promising framework for both semi-supervised learning and learning loss functions for predictors. For all tasks, the semi-supervised capability of DAN can significantly boost the predictor performance for small labeled sets with minor architecture changes across tasks. Moreover, the loss functions automatically learned by DANs are very competitive and usually outperform the standard pairwise and negative log-likelihood loss functions for both semi-supervised and supervised learning.

Connected through hyperedge HE-1 (induced by source paper "yes we gan applying adversarial techniques for autonomous driving"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "multimodal unsupervised image to image translation"
    - "universal adversarial training"
    - "large scale gan training for high fidelity natural image synthesis"
    - "learning to drive from simulation without real world labels"
    - "point cloud gan"

Connected through hyperedge HE-2 (induced by source paper "matching embeddings for domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "few shot unsupervised image to image translation"
    - "semi supervised domain adaptation via minimax entropy"
    - "deep variational transfer transfer learning through semi supervised deep generative models"
    - "deep visual domain adaptation a survey"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "casting geometric constraints in semantic segmentation as semi supervised learning"):
  with members:
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "open3d a modern library for 3d data processing"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "viable fast adaptation via backpropagating learned loss"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "alpha maml adaptive model agnostic meta learning"
    - "meta learning via learned loss"
    - "learning to teach with dynamic loss functions"
    - "fast context adaptation via meta learning"
    - "how to train your maml"
    - "evolved policy gradients"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-5 (induced by source paper "towards a deeper understanding of adversarial losses"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "mcgan mean and covariance feature matching gan"

Connected through hyperedge HE-6 (induced by source paper "s4 net geometry consistent semi supervised semantic segmentation"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "adversarial learning for semi supervised semantic segmentation"
    - "digging into self supervised monocular depth estimation"
    - "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"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"

Connected through hyperedge HE-7 (induced by source paper "semantic sentence matching with densely connected recurrent and co attentive information"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "natural language inference over interaction space"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "adaptive convolutional filter generation for natural language understanding"

Connected through hyperedge HE-8 (induced by source paper "multi task handwritten document layout analysis"):
  with members:
    - "dhsegment a generic deep learning approach for document segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

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)