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: "viewpoint invariant action recognition using rgb d videos"
  Abstract: In video-based action recognition, viewpoint variations often pose major challenges because the same actions can appear different from different views. We use the complementary RGB and Depth information from the RGB-D cameras to address this problem. The proposed technique capitalizes on the spatio-temporal information available in the two data streams to the extract action features that are largely insensitive to the viewpoint variations. We use the RGB data to compute dense trajectories that are translated to viewpoint insensitive deep features under a non-linear knowledge transfer model. Similarly, the Depth stream is used to extract CNN-based view invariant features on which Fourier Temporal Pyramid is computed to incorporate the temporal information. The heterogeneous features from the two streams are combined and used as a dictionary to predict the label of the test samples. To that end, we propose a sparse-dense collaborative representation classification scheme that strikes a balance between the discriminative abilities of the dense and the sparse representations of the samples over the extracted heterogeneous dictionary.

Connected through hyperedge HE-1 (induced by source paper "learning with privileged information via adversarial discriminative modality distillation"):
  with members:
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "modality distillation with multiple stream networks for action recognition"
    - "adversarial network compression"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "modality distillation with multiple stream networks for action recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "graph distillation for action detection with privileged modalities"
    - "unsupervised learning of long term motion dynamics for videos"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning a deep model for human action recognition from novel viewpoints"

Connected through hyperedge HE-3 (induced by source paper "graph distillation for action detection with privileged modalities"):
  with members:
    - "towards vision based smart hospitals a system for tracking and monitoring hand hygiene compliance"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "temporal action detection with structured segment networks"
    - "investigation of different skeleton features for cnn based 3d action recognition"
    - "miml fcn multi instance multi label learning via fully convolutional networks with privileged information"
    - "unsupervised learning of long term motion dynamics for videos"

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)