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: "generalized rank pooling for activity recognition"
  Abstract: Most popular deep models for action recognition split video sequences into short sub-sequences consisting of a few frames; frame-based features are then pooled for recognizing the activity. Usually, this pooling step discards the temporal order of the frames, which could otherwise be used for better recognition. Towards this end, we propose a novel pooling method, generalized rank pooling (GRP), that takes as input, features from the intermediate layers of a CNN that is trained on tiny sub-sequences, and produces as output the parameters of a subspace which (i) provides a low-rank approximation to the features and (ii) preserves their temporal order. We propose to use these parameters as a compact representation for the video sequence, which is then used in a classification setup. We formulate an objective for computing this subspace as a Riemannian optimization problem on the Grassmann manifold, and propose an efficient conjugate gradient scheme for solving it. Experiments on several activity recognition datasets show that our scheme leads to state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "a comparative review of recent kinect based action recognition algorithms"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "spatio temporal graph convolution for skeleton based 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"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "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 "unsupervised learning of action classes with continuous temporal embedding"):
  with members:
    - "unsupervised learning and segmentation of complex activities from video"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "spatio temporal channel correlation networks for action classification"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised video understanding by reconciliation of posture similarities"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-5 (induced by source paper "only time can tell discovering temporal data for temporal modeling"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "on the integration of optical flow and action recognition"
    - "from lifestyle vlogs to everyday interactions"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "rank pooling for action recognition"

Connected through hyperedge HE-6 (induced by source paper "learning discriminative video representations using adversarial perturbations"):
  with members:
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "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-7 (induced by source paper "video representation learning using discriminative pooling"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "ordered pooling of optical flow sequences for action recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-8 (induced by source paper "relational long short term memory for video action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "attention is all you need"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "lattice long short term memory for human 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)