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: "joint discovery of object states and manipulation actions"
  Abstract: Many human activities involve object manipulations aiming to modify the object state. Examples of common state changes include full/empty bottle, open/closed door, and attached/detached car wheel. In this work, we seek to automatically discover the states of objects and the associated manipulation actions. Given a set of videos for a particular task, we propose a joint model that learns to identify object states and to localize state-modifying actions. Our model is formulated as a discriminative clustering cost with constraints. We assume a consistent temporal order for the changes in object states and manipulation actions, and introduce new optimization techniques to learn model parameters without additional supervision. We demonstrate successful discovery of seven manipulation actions and corresponding object states on a new dataset of videos depicting real-life object manipulations. We show that our joint formulation results in an improvement of object state discovery by action recognition and vice versa.

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 "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 "activity driven weakly supervised object detection"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep self taught learning for weakly supervised object localization"

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 "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-6 (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"
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "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-7 (induced by source paper "explainable video action reasoning via prior knowledge and state transitions"):
  with members:
    - "referring relationships"
    - "what have we learned from deep representations for action recognition"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-8 (induced by source paper "joint object and state recognition using language knowledge"):
  with members:
    - "a survey of knowledge representation in service robotics"
    - "long activity video understanding using functional object oriented network"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "functional object oriented network for manipulation learning"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "boosting image captioning with attributes"
    - "cnn rnn a unified framework for multi label image classification"

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)