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: "procedure planning in instructional videos"
  Abstract: We propose a new challenging task: procedure planning in instructional videos. Unlike existing planning problems, where both the state and the action spaces are well-defined, the key challenge of planning in instructional videos is that both the state and the action spaces are open-vocabulary. We address this challenge with latent space planning, where we propose to explicitly leverage the constraints imposed by the conjugate relationships between states and actions in a learned plannable latent space. We evaluate both procedure planning and walkthrough planning on large-scale real-world instructional videos. Our experiments show that we are able to learn plannable semantic representations without explicit supervision. This enables sequential reasoning on real-world videos and leads to stronger generalization compared to existing planning approaches and neural network policies.

Connected through hyperedge HE-1 (induced by source paper "video2commonsense generating commonsense descriptions to enrich video captioning"):
  with members:
    - "vqa lol visual question answering under the lens of logic"
    - "lxmert learning cross modality encoder representations from transformers"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "blocksworld revisited learning and reasoning to generate event sequences from image pairs"
    - "roberta a robustly optimized bert pretraining approach"
    - "from recognition to cognition visual commonsense reasoning"
    - "end to end dense video captioning with masked transformer"

Connected through hyperedge HE-2 (induced by source paper "imitation learning for human pose prediction"):
  with members:
    - "peeking into the future predicting future person activities and locations in videos"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "action agnostic human pose forecasting"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning plannable representations with causal infogan"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-3 (induced by source paper "robot learning and execution of collaborative manipulation plans from youtube videos"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "unsupervised learning and segmentation of complex activities from video"
    - "yolov3 an incremental improvement"
    - "functional object oriented network construction expansion"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "towards automatic learning of procedures from web instructional 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)