human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream"):
  Abstract: Manipulation tasks in daily life, such as pouring water, unfold intentionally under specialized manipulation contexts. Being able to process contextual knowledge in these Activities of Daily Living (ADLs) over time can help us understand manipulation intentions, which are essential for an intelligent robot to transition smoothly between various manipulation actions. In this paper, to model the intended concepts of manipulation, we present a vision dataset under a strictly constrained knowledge domain for both robot and human manipulations, where manipulation concepts and relations are stored by an ontology system in a taxonomic manner. Furthermore, we propose a scheme to generate a combination of visual attentions and an evolving knowledge graph filled with commonsense knowledge. Our scheme works with real-world camera streams and fuses an attention-based Vision-Language model with the ontology system. The experimental results demonstrate that the proposed scheme can successfully represent the evolution of an intended object manipulation procedure for both robots and humans. The proposed scheme allows the robot to mimic human-like intentional behaviors by watching real-time videos. We aim to develop this scheme further for real-world robot intelligence in Human-Robot Interaction.

Includes members:
  Member "manipulation skill assessment from videos with spatial attention network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward adaptive guidance modeling the variety of user behaviors in continuous skill improving experiences of machine operation tasks")
  Member "combined task and action learning from human demonstrations for mobile manipulation applications":
    also connected through other hyperedges:
      (none)
  Member "v2cnet a deep learning framework to translate videos to commands for robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinearity compensation in a multi dof shoulder sensing exosuit for real time teleoperation")
  Member "robot learning and execution of collaborative manipulation plans from youtube videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vision based robot manipulation learning via human demonstrations")
  Member "integrating knowledge and reasoning in image understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from seeing to moving a survey on learning for visual indoor navigation vin")
  Member "improving grounded natural language understanding through human robot dialog":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vision and dialog navigation")
  Member "learning actions from human demonstration video for robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vision based robot manipulation learning via human demonstrations")
  Member "prospection interpretable plans from language by predicting the future":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transferable task execution from pixels through deep planning domain learning")

Question: Please tell me which class the source paper 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)