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: "difftaichi differentiable programming for physical simulation"
  Abstract: We present DiffTaichi, a new differentiable programming language tailored for building high-performance differentiable physical simulators. Based on an imperative programming language, DiffTaichi generates gradients of simulation steps using source code transformations that preserve arithmetic intensity and parallelism. A light-weight tape is used to record the whole simulation program structure and replay the gradient kernels in a reversed order, for end-to-end backpropagation. We demonstrate the performance and productivity of our language in gradient-based learning and optimization tasks on 10 different physical simulators. For example, a differentiable elastic object simulator written in our language is 4.2x shorter than the hand-engineered CUDA version yet runs as fast, and is 188x faster than the TensorFlow implementation. Using our differentiable programs, neural network controllers are typically optimized within only tens of iterations.

Connected through hyperedge HE-1 (induced by source paper "learning to simulate complex physics with graph networks"):
  with members:
    - "learning to control pdes with differentiable physics"
    - "dynamic graph convolutional networks"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "hamiltonian graph networks with ode integrators"
    - "scale mlperf 0 6 models on google tpu v3 pods"
    - "graph networks as learnable physics engines for inference and control"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-2 (induced by source paper "deep differentiable grasp planner for high dof grippers"):
  with members:
    - "generating grasp poses for a high dof gripper using neural networks"
    - "new formulation of mixed integer conic programming for globally optimal grasp planning"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "the surprising effectiveness of linear models for visual foresight in object pile manipulation"):
  with members:
    - "keto learning keypoint representations for tool manipulation"
    - "planning robot motion using deep visual prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "object centric forward modeling for model predictive control"
    - "learning to manipulate object collections using grounded state representations"
    - "friction variability in planar pushing data anisotropic friction and data collection bias"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "design validation and case studies of 2d vsr sim an optimization friendly simulator of 2 d voxel based soft robots"):
  with members:
    - "automated shapeshifting for function recovery in damaged robots"
    - "titan a parallel asynchronous library for multi agent and soft body robotics using nvidia cuda"
    - "scalable sim to real transfer of soft robot designs"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"

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)