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: "multiple instance curriculum learning for weakly supervised object detection"
  Abstract: When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e.g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects. To address this challenge, we incorporate object segmentation into the detector training, which guides the model to correctly localize the full objects. We propose the multiple instance curriculum learning (MICL) method, which injects curriculum learning (CL) into the multiple instance learning (MIL) framework. The MICL method starts by automatically picking the easy training examples, where the extent of the segmentation masks agree with detection bounding boxes. The training set is gradually expanded to include harder examples to train strong detectors that handle complex images. The proposed MICL method with segmentation in the loop outperforms the state-of-the-art weakly supervised object detectors by a substantial margin on the PASCAL VOC datasets.

Connected through hyperedge HE-1 (induced by source paper "hard pixel mining for depth privileged semantic segmentation"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-2 (induced by source paper "image difficulty curriculum for generative adversarial networks cugan"):
  with members:
    - "on line adaptative curriculum learning for gans"
    - "spectral normalization for generative adversarial networks"
    - "automated curriculum learning for neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "pacgan the power of two samples in generative adversarial networks"
    - "the numerics of gans"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-3 (induced by source paper "curriculum self paced learning for cross domain object detection"):
  with members:
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "a robust learning approach to domain adaptive object detection"
    - "beyond sharing weights for deep domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised visual representation learning by context prediction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "dissimilarity coefficient based weakly supervised object detection"):
  with members:
    - "zigzag learning for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "learning human poses from actions"
    - "collaborative learning for weakly supervised object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "saliency guided end to end learning for weakly supervised object detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-5 (induced by source paper "curriculum learning of visual attribute clusters for multi task classification"):
  with members:
    - "automated curriculum learning for neural networks"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "teacher student curriculum learning"
    - "curriculum learning for multi task classification of visual attributes"
    - "reverse curriculum generation for reinforcement learning"
    - "class rectification hard mining for imbalanced deep learning"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "cassl curriculum accelerated self supervised learning"

Connected through hyperedge HE-6 (induced by source paper "exploiting web images for weakly supervised object detection"):
  with members:
    - "zero annotation object detection with web knowledge transfer"
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"
    - "ssd single shot multibox detector"
    - "weakly supervised cascaded convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "a taught obesrve ask toa method for object detection with critical supervision"):
  with members:
    - "extreme clicking for efficient object annotation"
    - "weakly supervised object localization using things and stuff transfer"
    - "mining object parts from cnns via active question answering"
    - "ron reverse connection with objectness prior networks for object detection"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "training object class detectors with click supervision"
    - "training region based object detectors with online hard example mining"
    - "weakly supervised object localization using size estimates"

Connected through hyperedge HE-8 (induced by source paper "a fully convolutional tri branch network fctn for domain adaptation"):
  with members:
    - "asymmetric tri training for unsupervised domain adaptation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "playing for data ground truth from computer games"
    - "deep coral correlation alignment for deep domain adaptation"
    - "fcns in the wild pixel level adversarial and constraint based adaptation"
    - "proposal free network for instance level object 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)