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: "predicting privileged information for height estimation"
  Abstract: In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available at test time in real-life scenarios, we employ a learning using privileged information (LUPI) framework in a regression setup. Instead of using the LUPI paradigm for regression in its original form (i.e., \epsilon-SVR+), we train regression models that predict the privileged information at test time. The predictions are then used, along with observable features, to perform height estimation. Once the height is estimated, a mapping to classes is performed. We demonstrate that the proposed approach can estimate the height better and faster than the \epsilon-SVR+ algorithm and report results for different genders and quartiles of humans.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning of visual attribute clusters for multi task classification"):
  with members:
    - "automated curriculum learning for neural networks"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "teacher student curriculum learning"
    - "curriculum learning for multi task classification of visual attributes"
    - "reverse curriculum generation for reinforcement learning"
    - "class rectification hard mining for imbalanced deep learning"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-2 (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"
    - "deep transfer learning with joint adaptation networks"
    - "domain separation networks"
    - "cross modal distillation for supervision transfer"

Connected through hyperedge HE-3 (induced by source paper "curriculum learning for multi task classification of visual attributes"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "attributes for improved attributes a multi task network for attribute classification"
    - "contextual action recognition with r cnn"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "hd cnn hierarchical deep convolutional neural network for large scale visual recognition"

Connected through hyperedge HE-4 (induced by source paper "inferring human activities using robust privileged probabilistic learning"):
  with members:
    - "convolutional two stream network fusion for video action recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional 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)