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: "learning to transfer privileged information"
  Abstract: We introduce a learning framework called learning using privileged information (LUPI) to the computer vision field. We focus on the prototypical computer vision problem of teaching computers to recognize objects in images. We want the computers to be able to learn faster at the expense of providing extra information during training time. As additional information about the image data, we look at several scenarios that have been studied in computer vision before: attributes, bounding boxes and image tags. The information is privileged as it is available at training time but not at test time. We explore two maximum-margin techniques that are able to make use of this additional source of information, for binary and multiclass object classification. We interpret these methods as learning easiness and hardness of the objects in the privileged space and then transferring this knowledge to train a better classifier in the original space. We provide a thorough analysis and comparison of information transfer from privileged to the original data spaces for both LUPI methods. Our experiments show that incorporating privileged information can improve the classification accuracy. Finally, we conduct user studies to understand which samples are easy and which are hard for human learning, and explore how this information is related to easy and hard samples when learning a classifier.

Connected through hyperedge HE-1 (induced by source paper "deep learning under privileged information using heteroscedastic dropout"):
  with members:
    - "imagination improves multimodal translation"
    - "opennmt open source toolkit for neural machine translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "incorporating global visual features into attention based neural machine translation"
    - "neural machine translation with latent semantic of image and text"
    - "representation of linguistic form and function in recurrent neural networks"
    - "multimodal pivots for image caption translation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "incorporating privileged information to unsupervised anomaly detection"):
  with members:
    - "building better detection with privileged information"
    - "extending detection with forensic information"
    - "patterns for learning with side information"
    - "systematic construction of anomaly detection benchmarks from real data"
    - "privileged information for data clustering"

Connected through hyperedge HE-3 (induced by source paper "adaptive svm learning with privileged information for domain adaptation"):
  with members:
    - "training group orthogonal neural networks with privileged information"
    - "domain adaptation for visual applications a comprehensive survey"
    - "teacher student curriculum learning"
    - "adversarial discriminative domain adaptation"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "predicting privileged information for height estimation"
    - "deep transfer learning with joint adaptation networks"
    - "domain separation networks"

Connected through hyperedge HE-4 (induced by source paper "training group orthogonal neural networks with privileged information"):
  with members:
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "deep learning with s shaped rectified linear activation units"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "very deep convolutional networks for large scale image recognition"

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)