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: "bidirectional multirate reconstruction for temporal modeling in videos"
  Abstract: Despite the recent success of neural networks in image feature learning, a major problem in the video domain is the lack of sufficient labeled data for learning to model temporal information. In this paper, we propose an unsupervised temporal modeling method that learns from untrimmed videos. The speed of motion varies constantly, e.g., a man may run quickly or slowly. We therefore train a Multirate Visual Recurrent Model (MVRM) by encoding frames of a clip with different intervals. This learning process makes the learned model more capable of dealing with motion speed variance. Given a clip sampled from a video, we use its past and future neighboring clips as the temporal context, and reconstruct the two temporal transitions, i.e., present$\rightarrow$past transition and present$\rightarrow$future transition, reflecting the temporal information in different views. The proposed method exploits the two transitions simultaneously by incorporating a bidirectional reconstruction which consists of a backward reconstruction and a forward reconstruction. We apply the proposed method to two challenging video tasks, i.e., complex event detection and video captioning, in which it achieves state-of-the-art performance. Notably, our method generates the best single feature for event detection with a relative improvement of 10.4% on the MEDTest-13 dataset and achieves the best performance in video captioning across all evaluation metrics on the YouTube2Text dataset.

Connected through hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network"):
  with members:
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "few example object detection with model communication"
    - "mvx net multimodal voxelnet for 3d object detection"
    - "dafe fd density aware feature enrichment for face detection"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-2 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "an evaluation of action recognition models on epic kitchens"
    - "large scale weakly supervised pre training for video action recognition"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-3 (induced by source paper "searching for a robust neural architecture in four gpu hours"):
  with members:
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "graph hypernetworks for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"):
  with members:
    - "video description a survey of methods datasets and evaluation metrics"
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "memory augmented attention modelling for videos"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "video captioning with transferred semantic attributes"
    - "recurrent memory addressing for describing videos"
    - "semantic compositional networks for visual captioning"

Connected through hyperedge HE-5 (induced by source paper "cubic lstms for video prediction"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "motion prediction under multimodality with conditional stochastic networks"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "recurrent environment simulators"
    - "mocogan decomposing motion and content for video generation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "contrastive adaptation network for unsupervised domain adaptation"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "shakeout a new approach to regularized deep neural network training"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "multi adversarial domain adaptation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "in defense of the triplet loss for person re identification"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "dm gan dynamic memory generative adversarial networks for text to image synthesis"):
  with members:
    - "sim real joint reinforcement transfer for 3d indoor navigation"
    - "adversarial learning with local coordinate coding"
    - "spectral normalization for generative adversarial networks"
    - "text to image synthesis via symmetrical distillation networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "baidu uts submission to the epic kitchens action recognition challenge 2019"):
  with members:
    - "faster recurrent networks for efficient video classification"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "lsta long short term attention for egocentric action recognition"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "learnable pooling with context gating for video classification"

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)