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: "deep cross residual learning for multitask visual recognition"
  Abstract: Residual learning has recently surfaced as an effective means of constructing very deep neural networks for object recognition. However, current incarnations of residual networks do not allow for the modeling and integration of complex relations between closely coupled recognition tasks or across domains. Such problems are often encountered in multimedia applications involving large-scale content recognition. We propose a novel extension of residual learning for deep networks that enables intuitive learning across multiple related tasks using cross-connections called cross-residuals. These cross-residuals connections can be viewed as a form of in-network regularization and enables greater network generalization. We show how cross-residual learning (CRL) can be integrated in multitask networks to jointly train and detect visual concepts across several tasks. We present a single multitask cross-residual network with >40% less parameters that is able to achieve competitive, or even better, detection performance on a visual sentiment concept detection problem normally requiring multiple specialized single-task networks. The resulting multitask cross-residual network also achieves better detection performance by about 10.4% over a standard multitask residual network without cross-residuals with even a small amount of cross-task weighting.

Connected through hyperedge HE-1 (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-2 (induced by source paper "adashare learning what to share for efficient deep multi task learning"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "deep elastic networks with model selection for multi task learning"
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "many task learning with task routing"
    - "evolutionary architecture search for deep multitask networks"
    - "taskonomy disentangling task transfer learning"
    - "exploring shared structures and hierarchies for multiple nlp tasks"

Connected through hyperedge HE-3 (induced by source paper "efficient multi domain network learning by covariance normalization"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "boosting domain adaptation by discovering latent domains"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "efficient parametrization of multi domain deep neural networks"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "progressive neural networks"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "many task learning with task routing"):
  with members:
    - "dropblock a regularization method for convolutional networks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "shakedrop regularization for deep residual learning"

Connected through hyperedge HE-5 (induced by source paper "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "taskonomy disentangling task transfer learning"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "age progression regression by conditional adversarial autoencoder"

Connected through hyperedge HE-6 (induced by source paper "learning task relatedness in multi task learning for images in context"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "an overview of multi task learning in deep neural networks"
    - "omniart multi task deep learning for artistic data analysis"
    - "distributed multi task relationship learning"
    - "deep multi task representation learning a tensor factorisation approach"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"

Connected through hyperedge HE-7 (induced by source paper "natural disasters detection in social media and satellite imagery a survey"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "disaster monitoring using unmanned aerial vehicles and deep learning"
    - "retinal vessel segmentation in fundoscopic images with generative adversarial networks"
    - "enriching word vectors with subword information"
    - "large scale image retrieval with attentive deep local features"
    - "nazr cnn object detection and fine grained classification in crowdsourced uav images"
    - "yfcc100m the new data in multimedia research"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "pseudo task augmentation from deep multitask learning to intratask sharing and back"):
  with members:
    - "evolving deep neural networks"
    - "learned in translation contextualized word vectors"
    - "learning multiple visual domains with residual adapters"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "population based training of neural networks"
    - "distral robust multitask reinforcement learning"
    - "an overview of multi task learning in deep neural networks"
    - "one model to learn them all"

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)