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: "chainercv a library for deep learning in computer vision"
  Abstract: Despite significant progress of deep learning in the field of computer vision, there has not been a software library that covers these methods in a unifying manner. We introduce ChainerCV, a software library that is intended to fill this gap. ChainerCV supports numerous neural network models as well as software components needed to conduct research in computer vision. These implementations emphasize simplicity, flexibility and good software engineering practices. The library is designed to perform on par with the results reported in published papers and its tools can be used as a baseline for future research in computer vision. Our implementation includes sophisticated models like Faster R-CNN and SSD, and covers tasks such as object detection and semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "domain adaptation for object detection via style consistency"):
  with members:
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "photorealistic style transfer via wavelet transforms"
    - "semantic foggy scene understanding with synthetic data"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "a closed form solution to photorealistic image stylization"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "omnia faster r cnn detection in the wild through dataset merging and soft distillation"

Connected through hyperedge HE-3 (induced by source paper "crowd density forecasting by modeling patch based dynamics"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "discrete residual flow for probabilistic pedestrian behavior prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"

Connected through hyperedge HE-4 (induced by source paper "team pfdet s methods for open images challenge 2019"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "learning data augmentation strategies for object detection"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "squeeze and excitation networks"
    - "megdet a large mini batch object detector"

Connected through hyperedge HE-5 (induced by source paper "deep gated multi modal learning in hand object pose changes estimation using tactile and image data"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "bop benchmark for 6d object pose estimation"
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "segmentation driven 6d object pose estimation"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "robust deep multi modal learning based on gated information fusion network"
    - "interactively picking real world objects with unconstrained spoken language instructions"

Connected through hyperedge HE-6 (induced by source paper "recent advances in deep learning an overview"):
  with members:
    - "deep learning a critical appraisal"
    - "adversarial attacks on neural network policies"
    - "deep learning for environmentally robust speech recognition an overview of recent developments"
    - "lstm a search space odyssey"
    - "a knowledge grounded neural conversation model"
    - "me r cnn multi expert region based cnn for object detection"
    - "on the origin of deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-7 (induced by source paper "parallel grid pooling for data augmentation"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "feature space transfer for data augmentation"
    - "shakedrop regularization for deep residual learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "random erasing data augmentation"
    - "learning transferable architectures for scalable image recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"

Connected through hyperedge HE-8 (induced by source paper "cross domain weakly supervised object detection through progressive domain adaptation"):
  with members:
    - "focal loss for dense object detection"
    - "extreme clicking for efficient object annotation"
    - "adversarial discriminative domain adaptation"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multiple instance detection network with online instance classifier refinement"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"

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)