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 place new objects in a scene"
  Abstract: Placing is a necessary skill for a personal robot to have in order to perform tasks such as arranging objects in a disorganized room. The object placements should not only be stable but also be in their semantically preferred placing areas and orientations. This is challenging because an environment can have a large variety of objects and placing areas that may not have been seen by the robot before. #R##N#In this paper, we propose a learning approach for placing multiple objects in different placing areas in a scene. Given point-clouds of the objects and the scene, we design appropriate features and use a graphical model to encode various properties, such as the stacking of objects, stability, object-area relationship and common placing constraints. The inference in our model is an integer linear program, which we solve efficiently via an LP relaxation. We extensively evaluate our approach on 98 objects from 16 categories being placed into 40 areas. Our robotic experiments show a success rate of 98% in placing known objects and 82% in placing new objects stably. We use our method on our robots for performing tasks such as loading several dish-racks, a bookshelf and a fridge with multiple items.

Connected through hyperedge HE-1 (induced by source paper "learning object placements for relational instructions by hallucinating scene representations"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "fast scnn fast semantic segmentation network"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "referring relationships"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "context aware synthesis and placement of object instances"
    - "mattnet modular attention network for referring expression comprehension"
    - "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions"

Connected through hyperedge HE-2 (induced by source paper "improving robot success detection using static object data"):
  with members:
    - "robocse robot common sense embedding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "shifting the baseline single modality performance on visual navigation qa"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "a simple neural network module for relational reasoning"
    - "learning interpretable spatial operations in a rich 3d blocks world"

Connected through hyperedge HE-3 (induced by source paper "object placement planning and optimization for robot manipulators"):
  with members:
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "closed chain manipulation of large objects by multi arm robotic systems"
    - "a certified complete bimanual manipulation planner"
    - "data driven grasp synthesis a survey"

Connected through hyperedge HE-4 (induced by source paper "object placement on cluttered surfaces a nested local search approach"):
  with members:
    - "learning object arrangements in 3d scenes using human context"

Connected through hyperedge HE-5 (induced by source paper "a multimodal classifier generative adversarial network for carry and place tasks from ambiguous language instructions"):
  with members:
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-6 (induced by source paper "optimization beyond the convolution generalizing spatial relations with end to end metric learning"):
  with members:
    - "visuospatial skill learning for robots"
    - "metric learning for generalizing spatial relations to new objects"
    - "grounding spatio semantic referring expressions for human robot interaction"
    - "a differentiable physics engine for deep learning in robotics"
    - "a neural algorithm of artistic style"
    - "learning fine grained image similarity with deep ranking"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "metric learning for generalizing spatial relations to new objects"):
  with members:
    - "do what i want not what i did imitation of skills by planning sequences of actions"
    - "learning symbolic models of stochastic domains"

Connected through hyperedge HE-8 (induced by source paper "what happens if learning to predict the effect of forces in images"):
  with members:
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "dense optical flow prediction from a static image"
    - "deep residual learning for image recognition"
    - "action conditional video prediction using deep networks in atari games"
    - "learning visual predictive models of physics for playing billiards"
    - "end to end training of deep visuomotor policies"
    - "video language modeling a baseline for generative models of natural videos"

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.RO(Robotics)