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: "video representation learning using discriminative pooling"
  Abstract: Popular deep models for action recognition in videos generate independent predictions for short clips, which are then pooled heuristically to assign an action label to the full video segment. As not all frames may characterize the underlying action---indeed, many are common across multiple actions---pooling schemes that impose equal importance on all frames might be unfavorable. In an attempt to tackle this problem, we propose discriminative pooling, based on the notion that among the deep features generated on all short clips, there is at least one that characterizes the action. To this end, we learn a (nonlinear) hyperplane that separates this unknown, yet discriminative, feature from the rest. Applying multiple instance learning in a large-margin setup, we use the parameters of this separating hyperplane as a descriptor for the full video segment. Since these parameters are directly related to the support vectors in a max-margin framework, they serve as robust representations for pooling of the features. We formulate a joint objective and an efficient solver that learns these hyperplanes per video and the corresponding action classifiers over the hyperplanes. Our pooling scheme is end-to-end trainable within a deep framework. We report results from experiments on three benchmark datasets spanning a variety of challenges and demonstrate state-of-the-art performance across these tasks.

Connected through hyperedge HE-1 (induced by source paper "temporal reasoning graph for activity recognition"):
  with members:
    - "visual query answering by entity attribute graph matching and reasoning"
    - "linkage based face clustering via graph convolution network"
    - "multi label image recognition with graph convolutional networks"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "graph neural networks a review of methods and applications"
    - "learning discriminative video representations using adversarial perturbations"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-2 (induced by source paper "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "sphereface deep hypersphere embedding for face recognition"
    - "when kernel methods meet feature learning log covariance network for action recognition from skeletal data"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "pose conditioned spatio temporal attention for human action recognition"

Connected through hyperedge HE-3 (induced by source paper "discriminative video representation learning using support vector classifiers"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "a short note about kinetics 600"
    - "non linear temporal subspace representations for activity recognition"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "appearance and relation networks for video classification"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "cooperative cross stream network for discriminative action representation"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "on the effectiveness of task granularity for transfer learning"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "avd adversarial video distillation"):
  with members:
    - "deep discriminative model for video classification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "temporal segment networks for action recognition in videos"
    - "the kinetics human action video dataset"
    - "convnet architecture search for spatiotemporal feature learning"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "gods generalized one class discriminative subspaces for anomaly detection"):
  with members:
    - "learning deep features for one class classification"
    - "learning discriminative video representations using adversarial perturbations"
    - "subspace support vector data description"
    - "anomaly detection using one class neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "one class slab support vector machine"

Connected through hyperedge HE-7 (induced by source paper "learning discriminative video representations using adversarial perturbations"):
  with members:
    - "non linear temporal subspace representations for activity recognition"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "lattice long short term memory for human action recognition"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"

Connected through hyperedge HE-8 (induced by source paper "evolving space time neural architectures for videos"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "regularized evolution for image classifier architecture search"
    - "temporal gaussian mixture layer for videos"
    - "mnasnet platform aware neural architecture search for mobile"
    - "large scale evolution of image classifiers"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural 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)