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: "the open world of micro videos"
  Abstract: Micro-videos are six-second videos popular on social media networks with several unique properties. Firstly, because of the authoring process, they contain significantly more diversity and narrative structure than existing collections of video "snippets". Secondly, because they are often captured by hand-held mobile cameras, they contain specialized viewpoints including third-person, egocentric, and self-facing views seldom seen in traditional produced video. Thirdly, due to to their continuous production and publication on social networks, aggregate micro-video content contains interesting open-world dynamics that reflects the temporal evolution of tag topics. These aspects make micro-videos an appealing well of visual data for developing large-scale models for video understanding. We analyze a novel dataset of micro-videos labeled with 58 thousand tags. To analyze this data, we introduce viewpoint-specific and temporally-evolving models for video understanding, defined over state-of-the-art motion and deep visual features. We conclude that our dataset opens up new research opportunities for large-scale video analysis, novel viewpoints, and open-world dynamics.

Connected through hyperedge HE-1 (induced by source paper "oops predicting unintentional action in video"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "self supervised learning for video correspondence flow"
    - "gaze360 physically unconstrained gaze estimation in the wild"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "learning representations for predicting future activities"
    - "revisiting self supervised visual representation learning"
    - "back to the future knowledge distillation for human action anticipation"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised action localization with background modeling"):
  with members:
    - "autoloc weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-3 (induced by source paper "moments in time dataset one million videos for event understanding"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "from lifestyle vlogs to everyday interactions"
    - "look listen and learn"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "the something something video database for learning and evaluating visual common sense"
    - "temporal relational reasoning in videos"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "actor and observer joint modeling of first and third person videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "multimodal machine learning a survey and taxonomy"
    - "identifying first person camera wearers in third person videos"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "ego surfing first person videos"
    - "learning features by watching objects move"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "learning action maps of large environments via first person vision"

Connected through hyperedge HE-5 (induced by source paper "learning to localize and align fine grained actions to sparse instructions"):
  with members:
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "dense captioning events in videos"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "going deeper into first person activity recognition"
    - "end to end learning of action detection from frame glimpses in videos"
    - "unsupervised learning from narrated instruction videos"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "active testing an efficient and robust framework for estimating accuracy"):
  with members:
    - "efficient test collection construction via active learning"
    - "learning from noisy large scale datasets with minimal supervision"
    - "active sampling for large scale information retrieval evaluation"
    - "learning from noisy labels with distillation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep residual learning for image recognition"
    - "learning visual features from large weakly supervised data"

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"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "context encoders feature learning by inpainting"
    - "soundnet learning sound representations from unlabeled video"

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)