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: "adaptive svm learning with privileged information for domain adaptation"
  Abstract: Incorporating additional knowledge in the learning process can be beneficial for several computer vision and machine learning tasks. Whether privileged information originates from a source domain that is adapted to a target domain, or as additional features available at training time only, using such privileged (i.e., auxiliary) information is of high importance as it improves the recognition performance and generalization. However, both primary and privileged information are rarely derived from the same distribution, which poses an additional challenge to the recognition task. To address these challenges, we present a novel learning paradigm that leverages privileged information in a domain adaptation setup to perform visual recognition tasks. The proposed framework, named Adaptive SVM+, combines the advantages of both the learning using privileged information (LUPI) paradigm and the domain adaptation framework, which are naturally embedded in the objective function of a regular SVM. We demonstrate the effectiveness of our approach on the publicly available Animals with Attributes and INTERACT datasets and report state-of-the-art results in both of them.

Connected through hyperedge HE-1 (induced by source paper "hard pixel mining for depth privileged semantic segmentation"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-2 (induced by source paper "dada depth aware domain adaptation in semantic segmentation"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "spigan privileged adversarial learning from simulation"

Connected through hyperedge HE-3 (induced by source paper "distraction aware feature learning for human attribute recognition via coarse to fine attention mechanism"):
  with members:
    - "dynamic curriculum learning for imbalanced data classification"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "attribute aware attention model for fine grained representation learning"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "localization guided learning for pedestrian attribute recognition"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "fashion forward forecasting visual style in fashion"

Connected through hyperedge HE-4 (induced by source paper "deep imbalanced attribute classification using visual attention aggregation"):
  with members:
    - "deep visual attention prediction"
    - "harmonious attention network for person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "focal loss for dense object detection"
    - "curriculum learning for multi task classification of visual attributes"
    - "order free rnn with visual attention for multi label classification"
    - "face attention network an effective face detector for the occluded faces"

Connected through hyperedge HE-5 (induced by source paper "spigan privileged adversarial learning from simulation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning domain invariant subspace using domain features and independence maximization"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "modality distillation with multiple stream networks for action recognition"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-6 (induced by source paper "curriculum learning of visual attribute clusters for multi task classification"):
  with members:
    - "automated curriculum learning for neural networks"
    - "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"
    - "cassl curriculum accelerated self supervised learning"

Connected through hyperedge HE-7 (induced by source paper "few shot adversarial domain adaptation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "nips 2016 tutorial generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning what and where to draw"

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)