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: "temporal cycle consistency learning"
  Abstract: We introduce a self-supervised representation learning method based on the task of temporal alignment between videos. The method trains a network using temporal cycle consistency (TCC), a differentiable cycle-consistency loss that can be used to find correspondences across time in multiple videos. The resulting per-frame embeddings can be used to align videos by simply matching frames using the nearest-neighbors in the learned embedding space. #R##N#To evaluate the power of the embeddings, we densely label the Pouring and Penn Action video datasets for action phases. We show that (i) the learned embeddings enable few-shot classification of these action phases, significantly reducing the supervised training requirements; and (ii) TCC is complementary to other methods of self-supervised learning in videos, such as Shuffle and Learn and Time-Contrastive Networks. The embeddings are also used for a number of applications based on alignment (dense temporal correspondence) between video pairs, including transfer of metadata of synchronized modalities between videos (sounds, temporal semantic labels), synchronized playback of multiple videos, and anomaly detection. Project webpage: this https URL .

Connected through hyperedge HE-1 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "feedback network for image super resolution"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "adversarial spatio temporal learning for video deblurring"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"
    - "video to video synthesis"

Connected through hyperedge HE-2 (induced by source paper "listen to look action recognition by previewing audio"):
  with members:
    - "what makes training multi modal networks hard"
    - "learning to localize sound sources in visual scenes analysis and applications"
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "distinit learning video representations without a single labeled video"
    - "co separating sounds of visual objects"
    - "videobert a joint model for video and language representation learning"
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"

Connected through hyperedge HE-3 (induced by source paper "learning video representations using contrastive bidirectional transformer"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "lxmert learning cross modality encoder representations from transformers"
    - "contrastive multiview coding"
    - "unicoder vl a universal encoder for vision and language by cross modal pre training"
    - "learning correspondence from the cycle consistency of time"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "data efficient image recognition with contrastive predictive coding"
    - "bert has a mouth and it must speak bert as a markov random field language model"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised completion moment detection using temporal attention"):
  with members:
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "action completion a temporal model for moment detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "am i done predicting action progress in videos"

Connected through hyperedge HE-5 (induced by source paper "no representation without transformation"):
  with members:
    - "learning to discover novel visual categories via deep transfer clustering"
    - "unsupervised learning of object keypoints for perception and control"
    - "dynamics aware unsupervised discovery of skills"
    - "shaping belief states with generative environment models for rl"
    - "neural processes"
    - "neural predictive belief representations"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "towards a definition of disentangled representations"

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)