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: "class2str end to end latent hierarchy learning"
  Abstract: Deep neural networks for image classification typically consists of a convolutional feature extractor followed by a fully connected classifier network. The predicted and the ground truth labels are represented as one hot vectors. Such a representation assumes that all classes are equally dissimilar. However, classes have visual similarities and often form a hierarchy. Learning this latent hierarchy explicitly in the architecture could provide invaluable insights. We propose an alternate architecture to the classifier network called the Latent Hierarchy (LH) Classifier and an end to end learned Class2Str mapping which discovers a latent hierarchy of the classes. We show that for some of the best performing architectures on CIFAR and Imagenet datasets, the proposed replacement and training by LH classifier recovers the accuracy, with a fraction of the number of parameters in the classifier part. Compared to the previous work of HDCNN, which also learns a 2 level hierarchy, we are able to learn a hierarchy at an arbitrary number of levels as well as obtain an accuracy improvement on the Imagenet classification task over them. We also verify that many visually similar classes are grouped together, under the learnt hierarchy.

Connected through hyperedge HE-1 (induced by source paper "decision propagation networks for image classification"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "ensemble soft margin softmax loss for image classification"
    - "do convolutional neural networks learn class hierarchy"
    - "deep forest towards an alternative to deep neural networks"
    - "deep convolutional decision jungle for image classification"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "improving pairwise ranking for multi label image classification"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "universal semi supervised semantic segmentation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "the natural language decathlon multitask learning as question answering"
    - "taskonomy disentangling task transfer learning"
    - "partial adversarial domain adaptation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "adversarial learning for semi supervised 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.CV(Computer Vision and Pattern Recognition)