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: "affordancenet an end to end deep learning approach for object affordance detection"
  Abstract: We propose AffordanceNet, a new deep learning approach to simultaneously detect multiple objects and their affordances from RGB images. Our AffordanceNet has two branches: an object detection branch to localize and classify the object, and an affordance detection branch to assign each pixel in the object to its most probable affordance label. The proposed framework employs three key components for effectively handling the multiclass problem in the affordance mask: a sequence of deconvolutional layers, a robust resizing strategy, and a multi-task loss function. The experimental results on the public datasets show that our AffordanceNet outperforms recent state-of-the-art methods by a fair margin, while its end-to-end architecture allows the inference at the speed of 150ms per image. This makes our AffordanceNet is well suitable for real-time robotic applications. Furthermore, we demonstrate the effectiveness of AffordanceNet in different testing environments and in real robotic applications. The source code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"

Connected through hyperedge HE-2 (induced by source paper "instance segmentation of visible and occluded regions for finding and picking target from a pile of objects"):
  with members:
    - "probabilistic 3d multilabel real time mapping for multi object manipulation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "on pre trained image features and synthetic images for deep learning"
    - "synthesizing training data for object detection in indoor scenes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning from simulated and unsupervised images through adversarial training"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "learning to refine object segments"

Connected through hyperedge HE-3 (induced by source paper "a survey of knowledge representation in service robotics"):
  with members:
    - "long activity video understanding using functional object oriented network"
    - "the limits and potentials of deep learning for robotics"
    - "identifying object states in cooking related images"
    - "functional object oriented network construction expansion"
    - "blitznet a real time deep network for scene understanding"
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "dex net 3 0 computing robust robot suction grasp targets in point clouds using a new analytic model and deep learning"

Connected through hyperedge HE-4 (induced by source paper "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"):
  with members:
    - "object captioning and retrieval with natural language"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "real time 6dof pose relocalization for event cameras with stacked spatial lstm networks"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"

Connected through hyperedge HE-5 (induced by source paper "keto learning keypoint representations for tool manipulation"):
  with members:
    - "kpam keypoint affordances for category level robotic manipulation"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "robot bed making deep transfer learning using depth sensing of deformable fabric"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "multi task domain adaptation for deep learning of instance grasping from simulation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-6 (induced by source paper "enabling robots to understand incomplete natural language instructions using commonsense reasoning"):
  with members:
    - "improving grounded natural language understanding through human robot dialog"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "a simple method for commonsense reasoning"
    - "attention based natural language grounding by navigating virtual environment"
    - "deep contextualized word representations"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "swag a large scale adversarial dataset for grounded commonsense inference"

Connected through hyperedge HE-7 (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"
    - "learning to act properly predicting and explaining affordances from images"
    - "gplac generalizing vision based robotic skills using weakly labeled images"
    - "deep predictive policy training using reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "imagine that leveraging emergent affordances for tool synthesis in reaching tasks"):
  with members:
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "visual reinforcement learning with imagined goals"
    - "world models"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"

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)