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 relational reasoning in videos"
  Abstract: Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.

Connected through hyperedge HE-1 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "deep learning based aerial image classification for emergency response applications using unmanned aerial vehicles"
    - "a survey of deep learning based object detection"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"

Connected through hyperedge HE-2 (induced by source paper "multi modal domain adaptation for fine grained action recognition"):
  with members:
    - "temporal attentive alignment for large scale video domain adaptation"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "unsupervised domain adaptation through self supervision"
    - "adversarial cross domain action recognition with co attention"
    - "scaling egocentric vision the epic kitchens dataset"
    - "slowfast networks for video recognition"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-3 (induced by source paper "hierarchical conditional relation networks for video question answering"):
  with members:
    - "learning to reason with relational video representation for question answering"
    - "progressive attention memory network for movie story question answering"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "long term feature banks for detailed video understanding"
    - "bilinear attention networks"
    - "non local netvlad encoding for video classification"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "motion appearance co memory networks for video question answering"

Connected through hyperedge HE-4 (induced by source paper "rethinking motion representation residual frames with 3d convnets for better action recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video action recognition via neural architecture searching"
    - "video classification with channel separated convolutional networks"
    - "temporal bilinear networks for video action recognition"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "v4d 4d convolutional neural networks for video level representation learning"):
  with members:
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "learning video representations from correspondence proposals"
    - "eco efficient convolutional network for online video understanding"
    - "action4d real time action recognition in the crowd and clutter"
    - "compact generalized non local network"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-6 (induced by source paper "temporal interlacing network"):
  with members:
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "multi moments in time learning and interpreting models for multi action video understanding"
    - "a short note about kinetics 600"
    - "deformable convnets v2 more deformable better results"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-7 (induced by source paper "autofoley artificial synthesis of synchronized sound tracks for silent videos with deep learning"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "learning sight from sound ambient sound provides supervision for visual learning"
    - "multimodal machine learning a survey and taxonomy"
    - "fast slow recurrent neural networks"
    - "look listen and learn"
    - "aenet learning deep audio features for video analysis"
    - "video frame interpolation via adaptive separable convolution"
    - "visual to sound generating natural sound for videos in the wild"

Connected through hyperedge HE-8 (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"
    - "temporal cycle consistency learning"

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)