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: "higher order function networks for learning composable 3d object representations"
  Abstract: We present a method to represent 3D objects using higher order functions, where an object is encoded directly into the weights and biases of a small `mapping' network by a larger encoder network. This mapping network can be used to reconstruct 3D objects by applying its encoded transformation to points sampled from a simple canonical space. We first demonstrate that an encoder network can produce mappings that reconstruct objects from single images more accurately than state of the art point set reconstruction methods. Next, we show that our method yields meaningful gains for robot motion planning problems that use this object representation for collision avoidance. We also demonstrate that our formulation allows for a novel method of object interpolation in a latent function space, where we compose the roots of the reconstruction functions for various objects to generate new, coherent objects. Finally, we demonstrate the coding efficiency of our approach: encoding objects directly as a neural network is highly parameter efficient when compared with object representations that encode the object of interest as a latent vector `codeword'. Our smallest reconstruction network has only about 7000 parameters and shows reconstruction quality generally better than state-of-the-art codeword-based object representation architectures with millions of parameters.

Connected through hyperedge HE-1 (induced by source paper "robotic grasping through combined image based grasp proposal and 3d reconstruction"):
  with members:
    - "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pixels to plans learning non prehensile manipulation by imitating a planner"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "open3d a modern library for 3d data processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-2 (induced by source paper "higher order function networks for view planning and multi view reconstruction"):
  with members:
    - "self supervised 3d shape and viewpoint estimation from single images for robotics"
    - "pixels to plans learning non prehensile manipulation by imitating a planner"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "mask r cnn"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "revisiting active perception"

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)