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 object arrangements in 3d scenes using human context"
  Abstract: We consider the problem of learning object arrangements in a 3D scene. The key idea here is to learn how objects relate to human poses based on their affordances, ease of use and reachability. In contrast to modeling object-object relationships, modeling human-object relationships scales linearly in the number of objects. We design appropriate density functions based on 3D spatial features to capture this. We learn the distribution of human poses in a scene using a variant of the Dirichlet process mixture model that allows sharing of the density function parameters across the same object types. Then we can reason about arrangements of the objects in the room based on these meaningful human poses. In our extensive experiments on 20 different rooms with a total of 47 objects, our algorithm predicted correct placements with an average error of 1.6 meters from ground truth. In arranging five real scenes, it received a score of 4.3/5 compared to 3.7 for the best baseline method.

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 "resolving 3d human pose ambiguities with 3d scene constraints"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "estimating 3d motion and forces of person object interactions from monocular video"
    - "learning joint reconstruction of hands and manipulated objects"
    - "indoor scene understanding in 2 5 3d for autonomous agents a survey"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"
    - "open3d a modern library for 3d data processing"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-3 (induced by source paper "3d relnet joint object and relational network for 3d prediction"):
  with members:
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "cycada cycle consistent adversarial domain adaptation"
    - "a simple neural network module for relational reasoning"
    - "6 dof object pose from semantic keypoints"
    - "factoring shape pose and layout from the 2d image of a 3d scene"
    - "semantic scene completion from a single depth image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-4 (induced by source paper "understanding human context in 3d scenes by learning spatial affordances with virtual skeleton models"):
  with members:
    - "unstructured human activity detection from rgbd images"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "object placement on cluttered surfaces a nested local search approach"):
  with members:
    - "learning to place new objects in a scene"

Connected through hyperedge HE-6 (induced by source paper "learning task oriented grasping for tool manipulation from simulated self supervision"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "grasp pose detection in point clouds"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "end to end learning of semantic grasping"

Connected through hyperedge HE-7 (induced by source paper "semantic mapping with simultaneous object detection and localization"):
  with members:
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"
    - "multiple object detection tracking and long term dynamics learning in large 3d maps"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "da rnn semantic mapping with data associated recurrent neural networks"
    - "semantic robot programming for goal directed manipulation in cluttered scenes"
    - "meaningful maps with object oriented semantic mapping"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-8 (induced by source paper "analysis and modeling of 3d indoor scenes"):
  with members:
    - "shapenet an information rich 3d model repository"

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.LG(Machine Learning)