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: "unsupervised semantic parsing of video collections"
  Abstract: Human communication typically has an underlying structure. This is reflected in the fact that in many user generated videos, a starting point, ending, and certain objective steps between these two can be identified. In this paper, we propose a method for parsing a video into such semantic steps in an unsupervised way. The proposed method is capable of providing a semantic "storyline" of the video composed of its objective steps. We accomplish this using both visual and language cues in a joint generative model. The proposed method can also provide a textual description for each of the identified semantic steps and video segments. We evaluate this method on a large number of complex YouTube videos and show results of unprecedented quality for this intricate and impactful problem.

Connected through hyperedge HE-1 (induced by source paper "visual grounding in video for unsupervised word translation"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "learning to follow directions in street view"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "unsupervised multi modal neural machine translation"
    - "learning visual question answering by bootstrapping hard attention"
    - "lifelong learning for image captioning by asking natural language questions"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-2 (induced by source paper "howto100m learning a text video embedding by watching hundred million narrated video clips"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "unsupervised learning and segmentation of complex activities from video"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "temporal cycle consistency learning"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "action completion a temporal model for moment detection"
    - "playing hard exploration games by watching youtube"
    - "unsupervised learning and segmentation of complex activities from video"
    - "neighbourhood consensus networks"
    - "deep unsupervised learning of visual similarities"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "action modifiers learning from adverbs in instructional videos"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "weakly supervised video moment retrieval from text queries"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "human action adverb recognition adha dataset and a three stream hybrid model"
    - "adversarial soft detection based aggregation network for image retrieval"
    - "motion appearance co memory networks for video question answering"
    - "attributes as operators factorizing unseen attribute object compositions"

Connected through hyperedge HE-5 (induced by source paper "alfred a benchmark for interpreting grounded instructions for everyday tasks"):
  with members:
    - "what should i do now marrying reinforcement learning and symbolic planning"
    - "vision and dialog navigation"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "multi target embodied question answering"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning to navigate unseen environments back translation with environmental dropout"

Connected through hyperedge HE-6 (induced by source paper "tripping through time efficient localization of activities in videos"):
  with members:
    - "read watch and move reinforcement learning for temporally grounding natural language descriptions in videos"
    - "learning to localize and align fine grained actions to sparse instructions"
    - "multilevel language and vision integration for text to clip retrieval"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "attend and interact higher order object interactions for video understanding"
    - "gated attention architectures for task oriented language grounding"

Connected through hyperedge HE-7 (induced by source paper "procedure planning in instructional videos"):
  with members:
    - "cross task weakly supervised learning from instructional videos"
    - "videobert a joint model for video and language representation learning"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-8 (induced by source paper "a case study on combining asr and visual features for generating instructional video captions"):
  with members:
    - "learning video representations using contrastive bidirectional transformer"
    - "multimodal abstractive summarization for how2 videos"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "learning to localize and align fine grained actions to sparse instructions"
    - "a neural compositional paradigm for image captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "audio visual event localization in unconstrained videos"

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)