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: "charades ego a large scale dataset of paired third and first person videos"
  Abstract: In Actor and Observer we introduced a dataset linking the first and third-person video understanding domains, the Charades-Ego Dataset. In this paper we describe the egocentric aspect of the dataset and present annotations for Charades-Ego with 68,536 activity instances in 68.8 hours of first and third-person video, making it one of the largest and most diverse egocentric datasets available. Charades-Ego furthermore shares activity classes, scripts, and methodology with the Charades dataset, that consist of additional 82.3 hours of third-person video with 66,500 activity instances. Charades-Ego has temporal annotations and textual descriptions, making it suitable for egocentric video classification, localization, captioning, and new tasks utilizing the cross-modal nature of the data.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (induced by source paper "spatio temporal action graph networks"):
  with members:
    - "referring relationships"
    - "relational deep reinforcement learning"
    - "graph networks as learnable physics engines for inference and control"
    - "actor centric relation network"
    - "video action transformer network"
    - "relational inductive biases deep learning and graph networks"
    - "videos as space time region graphs"
    - "image generation from scene graphs"

Connected through hyperedge HE-3 (induced by source paper "assemblenet searching for multi stream neural connectivity in video architectures"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "holistic large scale video understanding"
    - "long term feature banks for detailed video understanding"
    - "moments in time dataset one million videos for event understanding"
    - "regularized evolution for image classifier architecture search"
    - "connectivity learning in multi branch networks"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-4 (induced by source paper "saliency prediction in the deep learning era successes limitations and future challenges"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "deep visual attention prediction"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "learning a saliency evaluation metric using crowdsourced perceptual judgments"
    - "deepvs a deep learning based video saliency prediction approach"
    - "pros and cons of gan evaluation measures"

Connected through hyperedge HE-5 (induced by source paper "bottom up attention models of"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "eml net an expandable multi layer network for saliency prediction"
    - "saliency benchmarking made easy separating models maps and metrics"

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)