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: "a modulation module for multi task learning with applications in image retrieval"
  Abstract: Multi-task learning has been widely adopted in many computer vision tasks to improve overall computation efficiency or boost the performance of individual tasks, under the assumption that those tasks are correlated and complementary to each other. However, the relationships between the tasks are complicated in practice, especially when the number of involved tasks scales up. When two tasks are of weak relevance, they may compete or even distract each other during joint training of shared parameters, and as a consequence undermine the learning of all the tasks. This will raise destructive interference which decreases learning efficiency of shared parameters and lead to low quality loss local optimum w.r.t. shared parameters. To address the this problem, we propose a general modulation module, which can be inserted into any convolutional neural network architecture, to encourage the coupling and feature sharing of relevant tasks while disentangling the learning of irrelevant tasks with minor parameters addition. Equipped with this module, gradient directions from different tasks can be enforced to be consistent for those shared parameters, which benefits multi-task joint training. The module is end-to-end learnable without ad-hoc design for specific tasks, and can naturally handle many tasks at the same time. We apply our approach on two retrieval tasks, face retrieval on the CelebA dataset [1] and product retrieval on the UT-Zappos50K dataset [2, 3], and demonstrate its advantage over other multi-task learning methods in both accuracy and storage efficiency.

Connected through hyperedge HE-1 (induced by source paper "mti net multi scale task interaction networks for multi task learning"):
  with members:
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "deep high resolution representation learning for visual recognition"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic feature pyramid networks"
    - "attentive single tasking of multiple tasks"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "multi task learning by a top down control network"):
  with members:
    - "panoptic segmentation"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "superposition of many models into one"
    - "many task learning with task routing"
    - "attentive single tasking of multiple tasks"
    - "top down feedback for crowd counting convolutional neural network"
    - "multi task learning as multi objective optimization"
    - "progressive neural networks"

Connected through hyperedge HE-3 (induced by source paper "deeper task specificity improves joint entity and relation extraction"):
  with members:
    - "span based joint entity and relation extraction with transformer pre training"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "branched multi task networks deciding what layers to share"
    - "entity relation extraction as multi turn question answering"
    - "a general framework for information extraction using dynamic span graphs"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "deep contextualized word representations"
    - "joint entity recognition and relation extraction as a multi head selection problem"

Connected through hyperedge HE-4 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"
    - "multi task learning as multi objective optimization"

Connected through hyperedge HE-5 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"

Connected through hyperedge HE-6 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-7 (induced by source paper "towards adversarially robust object detection"):
  with members:
    - "rethinking imagenet pre training"
    - "robust adversarial perturbation on deep proposal based models"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "feature denoising for improving adversarial robustness"
    - "yolov3 an incremental improvement"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "mdssd multi scale deconvolutional single shot detector for small objects"

Connected through hyperedge HE-8 (induced by source paper "multidepth single image depth estimation via multi task regression and classification"):
  with members:
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "learning monocular depth by distilling cross domain stereo networks"

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)