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: "weakly supervised learning of affordances"
  Abstract: Localizing functional regions of objects or affordances is an important aspect of scene understanding. In this work, we cast the problem of affordance segmentation as that of semantic image segmentation. In order to explore various levels of supervision, we introduce a pixel-annotated affordance dataset of 3090 images containing 9916 object instances with rich contextual information in terms of human-object interactions. We use a deep convolutional neural network within an expectation maximization framework to take advantage of weakly labeled data like image level annotations or keypoint annotations. We show that a further reduction in supervision is possible with a minimal loss in performance when human pose is used as context.

Connected through hyperedge HE-1 (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"
    - "affordancenet an end to end deep learning approach for object affordance detection"

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"
    - "visual affordance and function understanding a survey"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "detecting robotic affordances on novel objects with regional attention and attributes"):
  with members:
    - "kpam keypoint affordances for category level robotic manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "ocnet object context network for scene parsing"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "dual attention network for scene segmentation"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "context encoding for semantic segmentation"
    - "attention is all you need"

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"
    - "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"

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)