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: "lattice long short term memory for human action recognition"
  Abstract: Human actions captured in video sequences are three-dimensional signals characterizing visual appearance and motion dynamics. To learn action patterns, existing methods adopt Convolutional and/or Recurrent Neural Networks (CNNs and RNNs). CNN based methods are effective in learning spatial appearances, but are limited in modeling long-term motion dynamics. RNNs, especially Long Short-Term Memory (LSTM), are able to learn temporal motion dynamics. However, naively applying RNNs to video sequences in a convolutional manner implicitly assumes that motions in videos are stationary across different spatial locations. This assumption is valid for short-term motions but invalid when the duration of the motion is long. #R##N#In this work, we propose Lattice-LSTM (L2STM), which extends LSTM by learning independent hidden state transitions of memory cells for individual spatial locations. This method effectively enhances the ability to model dynamics across time and addresses the non-stationary issue of long-term motion dynamics without significantly increasing the model complexity. Additionally, we introduce a novel multi-modal training procedure for training our network. Unlike traditional two-stream architectures which use RGB and optical flow information as input, our two-stream model leverages both modalities to jointly train both input gates and both forget gates in the network rather than treating the two streams as separate entities with no information about the other. We apply this end-to-end system to benchmark datasets (UCF-101 and HMDB-51) of human action recognition. Experiments show that on both datasets, our proposed method outperforms all existing ones that are based on LSTM and/or CNNs of similar model complexities.

Connected through hyperedge HE-1 (induced by source paper "adaptive deep metric embeddings for person re identification under occlusions"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "svdnet for pedestrian retrieval"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-2 (induced by source paper "the benefits of synthetic data for action categorization"):
  with members:
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the kinetics human action video dataset"
    - "learning from synthetic data addressing domain shift for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "an information rich sampling technique over spatio temporal cnn for classification of human actions in videos"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "adam a method for stochastic optimization"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "temporal action localization using long short term dependency"):
  with members:
    - "weakly supervised visual instrument playing action detection in videos"
    - "w talc weakly supervised temporal activity localization and classification"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "compressed video action recognition"
    - "single shot temporal action detection"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-5 (induced by source paper "bio lstm a biomechanically inspired recurrent neural network for 3 d pedestrian pose and gait prediction"):
  with members:
    - "pedx benchmark dataset for metric 3d pose estimation of pedestrians in complex urban intersections"
    - "quaternet a quaternion based recurrent model for human motion"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "3d human pose estimation in rgbd images for robotic task learning"
    - "densepose dense human pose estimation in the wild"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "memory augmented temporal dynamic learning for action recognition"):
  with members:
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "motion appearance co memory networks for video question answering"
    - "learning dynamic memory networks for object tracking"
    - "memory warps for learning long term online video representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention clusters purely attention based local feature integration for video classification"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-7 (induced by source paper "early action prediction with generative adversarial networks"):
  with members:
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "visual forecasting by imitating dynamics in natural sequences"
    - "the pose knows video forecasting by generating pose futures"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "real time action recognition with enhanced motion vector cnns"
    - "generative adversarial imitation learning"
    - "semantic image inpainting with deep generative models"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-8 (induced by source paper "if ttn information fused temporal transformation network for video action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "compressed video action recognition"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "convnet architecture search for spatiotemporal feature learning"
    - "optical flow guided feature a fast and robust motion representation for video action recognition"
    - "temporal relational reasoning in videos"
    - "temporal segment networks towards good practices for deep 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)