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: "learning to act properly predicting and explaining affordances from images"
  Abstract: We address the problem of affordance reasoning in diverse scenes that appear in the real world. Affordances relate the agent's actions to their effects when taken on the surrounding objects. In our work, we take the egocentric view of the scene, and aim to reason about action-object affordances that respect both the physical world as well as the social norms imposed by the society. We also aim to teach artificial agents why some actions should not be taken in certain situations, and what would likely happen if these actions would be taken. We collect a new dataset that builds upon ADE20k, referred to as ADE-Affordance, which contains annotations enabling such rich visual reasoning. We propose a model that exploits Graph Neural Networks to propagate contextual information from the scene in order to perform detailed affordance reasoning about each object. Our model is showcased through various ablation studies, pointing to successes and challenges in this complex task.

Connected through hyperedge HE-1 (induced by source paper "what object should i use task driven object detection"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "graph r cnn for scene graph generation"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "videos as space time region graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "soft nms improving object detection with one line of code"
    - "visual question answering as a meta learning task"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-2 (induced by source paper "generating 3d people in scenes without people"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning elementary structures for 3d shape generation and matching"
    - "does computer vision matter for action"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "the replica dataset a digital replica of indoor spaces"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"

Connected through hyperedge HE-3 (induced by source paper "putting humans in a scene learning affordance in 3d indoor environments"):
  with members:
    - "neural inverse rendering of an indoor scene from a single image"
    - "binge watching scaling affordance learning from sitcoms"
    - "virtualhome simulating household activities via programs"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "unified perceptual parsing for scene understanding"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "affordance learning for end to end visuomotor robot control"):
  with members:
    - "visual affordance and function understanding a survey"
    - "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"
    - "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 "generating natural language explanations for visual question answering using scene graphs and visual attention"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "attentive explanations justifying decisions and pointing to the evidence extended abstract"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "generating visual explanations"
    - "question relevance in vqa identifying non visual and false premise questions"
    - "exploring the limits of language modeling"

Connected through hyperedge HE-6 (induced by source paper "is that a chair imagining affordances using simulations of an articulated human body"):
  with members:
    - "kpam keypoint affordances for category level robotic manipulation"
    - "a deeper look at 3d shape classifiers"
    - "what can i do here leveraging deep 3d saliency and geometry for fast and scalable multiple affordance detection"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "what can i do around here deep functional scene understanding for cognitive robots"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-7 (induced by source paper "egocentric affordance detection with the one shot geometry driven interaction tensor"):
  with members:
    - "what can i do here leveraging deep 3d saliency and geometry for fast and scalable multiple affordance detection"

Connected through hyperedge HE-8 (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"
    - "visual affordance and function understanding a survey"
    - "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"

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)