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: "learning temporal embeddings for complex video analysis"
  Abstract: In this paper, we propose to learn temporal embeddings of video frames for complex video analysis. Large quantities of unlabeled video data can be easily obtained from the Internet. These videos possess the implicit weak label that they are sequences of temporally and semantically coherent images. We leverage this information to learn temporal embeddings for video frames by associating frames with the temporal context that they appear in. To do this, we propose a scheme for incorporating temporal context based on past and future frames in videos, and compare this to other contextual representations. In addition, we show how data augmentation using multi-resolution samples and hard negatives helps to significantly improve the quality of the learned embeddings. We evaluate various design decisions for learning temporal embeddings, and show that our embeddings can improve performance for multiple video tasks such as retrieval, classification, and temporal order recovery in unconstrained Internet video.

Connected through hyperedge HE-1 (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"
    - "generalized rank pooling for activity recognition"
    - "unsupervised video understanding by reconciliation of posture similarities"

Connected through hyperedge HE-2 (induced by source paper "meta amortized variational inference and learning"):
  with members:
    - "attentive neural processes"
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "bayesian model agnostic meta learning"
    - "large scale gan training for high fidelity natural image synthesis"
    - "variational memory addressing in generative models"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "form2fit learning shape priors for generalizable assembly from disassembly"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "learning pose estimation for high precision robotic assembly using simulated depth images"
    - "learning robotic assembly from cad"
    - "tom net learning transparent object matting from a single image"
    - "time reversal as self supervision"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-4 (induced by source paper "leveraging structural context models and ranking score fusion for human interaction prediction"):
  with members:
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "beyond short snippets deep networks for video classification"
    - "crf learning with cnn features for image segmentation"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-5 (induced by source paper "tile2vec unsupervised representation learning for spatially distributed data"):
  with members:
    - "conditional image generation with pixelcnn decoders"
    - "densely connected convolutional networks"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "pixel recurrent neural networks"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "adaptive monte carlo optimization"):
  with members:
    - "billion scale similarity search with gpus"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "generating videos with scene dynamics"):
  with members:
    - "unsupervised learning for physical interaction through video prediction"
    - "ambient sound provides supervision for visual learning"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "generative image modeling using style and structure adversarial networks"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "the open world of micro videos"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-8 (induced by source paper "object centric representation learning from unlabeled videos"):
  with members:
    - "context encoders feature learning by inpainting"
    - "learning image representations equivariant to ego motion"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "unsupervised feature learning from temporal data"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning image representations tied to ego motion"

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)