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 with privileged information via adversarial discriminative modality distillation"
  Abstract: Heterogeneous data modalities can provide complementary cues for several tasks, usually leading to more robust algorithms and better performance. However, while training data can be accurately collected to include a variety of sensory modalities, it is often the case that not all of them are available in real life (testing) scenarios, where a model has to be deployed. This raises the challenge of how to extract information from multimodal data in the training stage, in a form that can be exploited at test time, considering limitations such as noisy or missing modalities. This paper presents a new approach in this direction for RGB-D vision tasks, developed within the adversarial learning and privileged information frameworks. We consider the practical case of learning representations from depth and RGB videos, while relying only on RGB data at test time. We propose a new approach to train a hallucination network that learns to distill depth information via adversarial learning, resulting in a clean approach without several losses to balance or hyperparameters. We report state-of-the-art results for object classification on the NYUD dataset, and video action recognition on the largest multimodal dataset available for this task, the NTU RGB+D, as well as on the Northwestern-UCLA.

Connected through hyperedge HE-1 (induced by source paper "videossl semi supervised learning for video classification"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "only time can tell discovering temporal data for temporal modeling"
    - "distinit learning video representations without a single labeled video"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "video representation learning by dense predictive coding"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "semi supervised convolutional neural networks for human activity recognition"
    - "parallel separable 3d convolution for video and volumetric data understanding"

Connected through hyperedge HE-2 (induced by source paper "input dropout for spatially aligned modalities"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "modality distillation with multiple stream networks for action recognition"
    - "a framework for evaluating 6 dof object trackers"
    - "focal loss for dense object detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "choosing smartly adaptive multimodal fusion for object detection in changing environments"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "dmcl distillation multiple choice learning for multimodal action recognition"):
  with members:
    - "unifying heterogeneous classifiers with distillation"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "human action recognition and prediction a survey"
    - "snapshot distillation teacher student optimization in one generation"
    - "action recognition for depth video using multi view dynamic images"
    - "motion feature network fixed motion filter for action recognition"
    - "modality distillation with multiple stream networks for action recognition"
    - "representation flow for action recognition"

Connected through hyperedge HE-4 (induced by source paper "flow distilled ip two stream networks for compressed video action recognition"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "representation flow for action recognition"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "compressed video action 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)