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: "regularizing deep networks by modeling and predicting label structure"
  Abstract: We construct custom regularization functions for use in supervised training of deep neural networks. Our technique is applicable when the ground-truth labels themselves exhibit internal structure; we derive a regularizer by learning an autoencoder over the set of annotations. Training thereby becomes a two-phase procedure. The first phase models labels with an autoencoder. The second phase trains the actual network of interest by attaching an auxiliary branch that must predict output via a hidden layer of the autoencoder. After training, we discard this auxiliary branch. #R##N#We experiment in the context of semantic segmentation, demonstrating this regularization strategy leads to consistent accuracy boosts over baselines, both when training from scratch, or in combination with ImageNet pretraining. Gains are also consistent over different choices of convolutional network architecture. As our regularizer is discarded after training, our method has zero cost at test time; the performance improvements are essentially free. We are simply able to learn better network weights by building an abstract model of the label space, and then training the network to understand this abstraction alongside the original task.

Connected through hyperedge HE-1 (induced by source paper "task assisted domain adaptation with anchor tasks"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "taskonomy disentangling task transfer learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "adversarial structure matching for structured prediction tasks"):
  with members:
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "taskonomy disentangling task transfer learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "interpreting deep visual representations via network dissection"
    - "learning affinity via spatial propagation networks"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"

Connected through hyperedge HE-3 (induced by source paper "anchor tasks inexpensive shared and aligned tasks for domain adaptation"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "taskonomy disentangling task transfer learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "perspective guided convolution networks for crowd counting"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "adaptive affinity fields for semantic segmentation"):
  with members:
    - "learning affinity via spatial propagation networks"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "high performance semantic segmentation using very deep fully convolutional networks"
    - "deep learning markov random field for semantic segmentation"
    - "semantic segmentation using adversarial 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)