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 "constructing dynamic knowledge graph for visual semantic understanding and applications in autonomous robotics"):
  Abstract: Interpreting semantic knowledge describing entities, relations and attributes explicitly with visuals and implicitly with in behind-scene common senses gain more attention in autonomous robotics. By incorporating vision and language modeling with common-sense knowledge, we can provide rich features indicating strong semantic meanings for human and robot action relationships, which can be utilized further in autonomous robotic controls. In this paper, we propose a systematic scheme to generate high-conceptual dynamic knowledge graphs representing Entity-Relation-Entity (E-R-E) and Entity-Attribute-Value (E-A-V) knowledges by "watching" a video clip. A combination of Vision-Language model and static ontology tree is used to illustrate workspace, configurations, functions and usages for both human and robot. The proposed method is flexible and well-versed. It will serve as our first positioning investigation for further research in various applications for autonomous robots.

Includes members:
  Member "visual query answering by entity attribute graph matching and reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "temporal reasoning graph for activity recognition")
  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 "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream")
  Member "on exploring undetermined relationships for visual relationship detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "visual relationship detection with low rank non negative tensor decomposition")
  Member "gqa a new dataset for real world visual reasoning and compositional question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unbiased scene graph generation from biased training")
  Member "pointing novel objects in image captioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to learn words from visual scenes")
  Member "improving grounded natural language understanding through human robot dialog":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream")
  Member "context and attribute grounded dense captioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to collocate neural modules for image captioning")
  Member "connecting touch and vision via cross modal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual tactile fusion object clustering")

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)