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 "visual affordance and function understanding a survey"):
  Abstract: Nowadays, robots are dominating the manufacturing, entertainment and healthcare industries. Robot vision aims to equip robots with the ability to discover information, understand it and interact with the environment. These capabilities require an agent to effectively understand object affordances and functionalities in complex visual domains. In this literature survey, we first focus on Visual affordances and summarize the state of the art as well as open problems and research gaps. Specifically, we discuss sub-problems such as affordance detection, categorization, segmentation and high-level reasoning. Furthermore, we cover functional scene understanding and the prevalent functional descriptors used in the literature. The survey also provides necessary background to the problem, sheds light on its significance and highlights the existing challenges for affordance and functionality learning.

Includes members:
  Member "binge watching scaling affordance learning from sitcoms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video")
  Member "geometric affordances from a single example via the interaction tensor":
    also connected through other hyperedges:
      (none)
  Member "predicting human activities using stochastic grammar":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vrgym a virtual testbed for physical and interactive ai")
  Member "learning social affordance grammar from videos transferring human interactions to human robot interactions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vrgym a virtual testbed for physical and interactive ai")
  Member "deep affordance grounded sensorimotor object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "forecasting human object interaction joint prediction of motor attention and egocentric activity")
  Member "transfer of tool affordance and manipulation cues with 3d vision data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "alet automated labeling of equipment and tools a dataset a baseline and a usecase for tool detection in the wild")
  Member "adaptive binarization for weakly supervised affordance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "detecting robotic affordances on novel objects with regional attention and attributes")
  Member "learning to act properly predicting and explaining affordances from images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what object should i use task driven object detection")

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)