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: "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.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "collaborative annotation of semantic objects in images with multi granularity supervisions"

Connected through hyperedge HE-2 (induced by source paper "scene understanding for autonomous manipulation with deep learning"):
  with members:
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "video captioning with transferred semantic attributes"
    - "weakly supervised learning of affordances"

Connected through hyperedge HE-3 (induced by source paper "unsupervised primitive discovery for improved 3d generative modeling"):
  with members:
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "so net self organizing network for point cloud analysis"
    - "volumetric convolution automatic representation learning in unit ball"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "affordance learning for end to end visuomotor robot control"):
  with members:
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "deep object centric representations for generalizable robot learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "learning to act properly predicting and explaining affordances from images"
    - "gplac generalizing vision based robotic skills using weakly labeled images"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "deep predictive policy training using reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "grounded human object interaction hotspots from video extended abstract"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "scaling egocentric vision the epic kitchens dataset"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep multi level network for saliency prediction"
    - "deepgaze ii reading fixations from deep features trained on object recognition"
    - "what do different evaluation metrics tell us about saliency models"

Connected through hyperedge HE-6 (induced by source paper "grounded human object interaction hotspots from video"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "binge watching scaling affordance learning from sitcoms"
    - "scaling egocentric vision the epic kitchens dataset"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "joint discovery of object states and manipulation actions"
    - "dual motion gan for future flow embedded video prediction"
    - "self supervised visual planning with temporal skip connections"
    - "learning to generate long term future via hierarchical prediction"

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)