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 visual linguistic reference resolution in instructional videos"
  Abstract: We propose an unsupervised method for reference resolution in instructional videos, where the goal is to temporally link an entity (e.g., "dressing") to the action (e.g., "mix yogurt") that produced it. The key challenge is the inevitable visual-linguistic ambiguities arising from the changes in both visual appearance and referring expression of an entity in the video. This challenge is amplified by the fact that we aim to resolve references with no supervision. We address these challenges by learning a joint visual-linguistic model, where linguistic cues can help resolve visual ambiguities and vice versa. We verify our approach by learning our model unsupervisedly using more than two thousand unstructured cooking videos from YouTube, and show that our visual-linguistic model can substantially improve upon state-of-the-art linguistic only model on reference resolution in instructional videos.

Connected through hyperedge HE-1 (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-2 (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-3 (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"

Connected through hyperedge HE-4 (induced by source paper "coin a large scale dataset for comprehensive instructional video analysis"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "end to end dense video captioning with masked transformer"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "diversity aware multi video summarization"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "temporal action detection with structured segment networks"
    - "dense captioning events in videos"

Connected through hyperedge HE-5 (induced by source paper "cross task weakly supervised learning from instructional videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "unsupervised learning and segmentation of complex activities from video"
    - "deep clustering for unsupervised learning of visual features"
    - "joint discovery of object states and manipulation actions"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "from lifestyle vlogs to everyday interactions"

Connected through hyperedge HE-6 (induced by source paper "reward learning from narrated demonstrations"):
  with members:
    - "multi modal imitation learning from unstructured demonstrations using generative adversarial nets"
    - "hindsight experience replay"
    - "generating descriptions with grounded and co referenced people"
    - "deep object centric representations for generalizable robot learning"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "third person imitation learning"

Connected through hyperedge HE-7 (induced by source paper "learning to localize and align fine grained actions to sparse instructions"):
  with members:
    - "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"
    - "the open world of micro videos"
    - "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-8 (induced by source paper "how to make a blt sandwich learning to reason towards understanding web instructional videos"):
  with members:
    - "end to end dense video captioning with masked transformer"
    - "compositional attention networks for machine reasoning"
    - "videos as space time region graphs"
    - "weakly supervised visual grounding of phrases with linguistic structures"
    - "towards automatic learning of procedures from web instructional videos"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "leveraging video descriptions to learn video question answering"
    - "uncovering temporal context for video question and answering"

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)