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: "se3 nets learning rigid body motion using deep neural networks"
  Abstract: We introduce SE3-Nets, which are deep networks designed to model rigid body motion from raw point cloud data. Based only on pairs of depth images along with an action vector and point wise data associations, SE3-Nets learn to segment effected object parts and predict their motion resulting from the applied force. Rather than learning point wise flow vectors, SE3-Nets predict SE3 transformations for different parts of the scene. Using simulated depth data of a table top scene and a robot manipulator, we show that the structure underlying SE3-Nets enables them to generate a far more consistent prediction of object motion than traditional flow based networks.

Connected through hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "conditional invertible flow for point cloud generation"
    - "grip graph based interaction aware trajectory prediction"
    - "probabilistic reconstruction networks for 3d shape inference from a single image"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "human motion trajectory prediction a survey"
    - "pixor real time 3d object detection from point clouds"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-2 (induced by source paper "predicting the physical dynamics of unseen 3d objects"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "learning generalizable physical dynamics of 3d rigid objects"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "chainqueen a real time differentiable physical simulator for soft robotics"

Connected through hyperedge HE-3 (induced by source paper "good robot efficient reinforcement learning for multi step visual tasks with sim to real transfer"):
  with members:
    - "6 dof grasping for target driven object manipulation in clutter"
    - "form2fit learning shape priors for generalizable assembly from disassembly"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-4 (induced by source paper "let s push things forward a survey on robot pushing"):
  with members:
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "feature based transfer learning for robotic push manipulation"
    - "a data efficient approach to precise and controlled pushing"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "deep predictive policy training using reinforcement learning"
    - "a probabilistic data driven model for planar pushing"

Connected through hyperedge HE-5 (induced by source paper "learning to singulate objects using a push proposal network"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "metric learning for generalizing spatial relations to new objects"
    - "interactive perception leveraging action in perception and perception in action"
    - "fast model identification via physics engines for data efficient policy search"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "deep visual foresight for planning robot motion"
    - "high precision grasp pose detection in dense clutter"

Connected through hyperedge HE-6 (induced by source paper "dynamic slam the need for speed"):
  with members:
    - "deep robust single image depth estimation neural network using scene understanding"
    - "cubeslam monocular 3 d object slam"
    - "camera based vehicle velocity estimation from monocular video"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "mask r cnn"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-7 (induced by source paper "belief regulated dual propagation nets for learning action effects on groups of articulated objects"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "propagation networks for model based control under partial observation"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning physical intuition of block towers by example"
    - "what happens if learning to predict the effect of forces in images"
    - "interaction networks for learning about objects relations and physics"
    - "a compositional object based approach to learning physical dynamics"

Connected through hyperedge HE-8 (induced by source paper "dissipative symoden encoding hamiltonian dynamics with dissipation and control into deep learning"):
  with members:
    - "symplectic ode net learning hamiltonian dynamics with control"
    - "a general framework for structured learning of mechanical systems"
    - "learning dynamical systems from partial observations"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "hamiltonian neural networks"
    - "continuous control with deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "adam a method for stochastic optimization"

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)