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: "multi task learning by deep collaboration and application in facial landmark detection"
  Abstract: Convolutional neural networks (CNN) have become the most successful and popular approach in many vision-related domains. While CNNs are particularly well-suited for capturing a proper hierarchy of concepts from real-world images, they are limited to domains where data is abundant. Recent attempts have looked into mitigating this data scarcity problem by casting their original single-task problem into a new multi-task learning (MTL) problem. The main goal of this inductive transfer mechanism is to leverage domain-specific information from related tasks, in order to improve generalization on the main task. While recent results in the deep learning (DL) community have shown the promising potential of training task-specific CNNs in a soft parameter sharing framework, integrating the recent DL advances for improving knowledge sharing is still an open problem. In this paper, we propose the Deep Collaboration Network (DCNet), a novel approach for connecting task-specific CNNs in a MTL framework. We define connectivity in terms of two distinct non-linear transformation blocks. One aggregates task-specific features into global features, while the other merges back the global features with each task-specific network. Based on the observation that task relevance depends on depth, our transformation blocks use skip connections as suggested by residual network approaches, to more easily deactivate unrelated task-dependent features. To validate our approach, we employed facial landmark detection (FLD) datasets as they are readily amenable to MTL, given the number of tasks they include. Experimental results show that we can achieve up to 24.31% relative improvement in landmark failure rate over other state-of-the-art MTL approaches. We finally perform an ablation study showing that our approach effectively allows knowledge sharing, by leveraging domain-specific features at particular depths from tasks that we know are related.

Connected through hyperedge HE-1 (induced by source paper "discovering differential features adversarial learning for information credibility evaluation"):
  with members:
    - "the role of user profile for fake news detection"
    - "different absorption from the same sharing sifted multi task learning for fake news detection"
    - "multinomial adversarial networks for multi domain text classification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake cures user centric modeling of health misinformation in social media"
    - "deep neural networks for bot detection"
    - "neural networks for information retrieval"
    - "like sheep among wolves characterizing hateful users on twitter"

Connected through hyperedge HE-2 (induced by source paper "deep multimodality model for multi task multi view learning"):
  with members:
    - "a survey on multi task learning"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks for sentence classification"
    - "deep learning face attributes in the wild"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "nddr cnn layer wise feature fusing in multi task cnn by neural discriminative dimensionality reduction"):
  with members:
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "squeeze and excitation networks"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "an overview of multi task learning in deep neural networks"
    - "joint multi person pose estimation and semantic part segmentation"
    - "mask r cnn"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"

Connected through hyperedge HE-4 (induced by source paper "tree species identification from bark images using convolutional neural networks"):
  with members:
    - "uav visual teach and repeat using only semantic object features"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "a survey on multi task learning"
    - "learning rotation invariant convolutional filters for texture classification"
    - "relay backpropagation for effective learning of deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)