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: "predicting the dynamics of 2d objects with a deep residual network"
  Abstract: We investigate how a residual network can learn to predict the dynamics of interacting shapes purely as an image-to-image regression problem. #R##N#With a simple 2d physics simulator, we generate short sequences composed of rectangles put in motion by applying a pulling force at a point picked at random. The network is trained with a quadratic loss to predict the image of the resulting configuration, given the image of the starting configuration and an image indicating the point of grasping. #R##N#Experiments show that the network learns to predict accurately the resulting image, which implies in particular that (1) it segments rectangles as distinct components, (2) it infers which one contains the grasping point, (3) it models properly the dynamic of a single rectangle, including the torque, (4) it detects and handles collisions to some extent, and (5) it re-synthesizes properly the entire scene with displaced rectangles.

Connected through hyperedge HE-1 (induced by source paper "accelerating eulerian fluid simulation with convolutional networks"):
  with members:
    - "se3 nets learning rigid body motion using deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "language modeling with gated convolutional networks"
    - "learning physical intuition of block towers by example"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"

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.CV(Computer Vision and Pattern Recognition)