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: "3d physnet learning the intuitive physics of non rigid object deformations"
  Abstract: The ability to interact and understand the environment is a fundamental prerequisite for a wide range of applications from robotics to augmented reality. In particular, predicting how deformable objects will react to applied forces in real time is a significant challenge. This is further confounded by the fact that shape information about encountered objects in the real world is often impaired by occlusions, noise and missing regions e.g. a robot manipulating an object will only be able to observe a partial view of the entire solid. In this work we present a framework, 3D-PhysNet, which is able to predict how a three-dimensional solid will deform under an applied force using intuitive physics modelling. In particular, we propose a new method to encode the physical properties of the material and the applied force, enabling generalisation over materials. The key is to combine deep variational autoencoders with adversarial training, conditioned on the applied force and the material properties. We further propose a cascaded architecture that takes a single 2.5D depth view of the object and predicts its deformation. Training data is provided by a physics simulator. The network is fast enough to be used in real-time applications from partial views. Experimental results show the viability and the generalisation properties of the proposed architecture.

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"

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"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "chainqueen a real time differentiable physical simulator for soft robotics"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-3 (induced by source paper "learning generalizable physical dynamics of 3d rigid objects"):
  with members:
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "physical primitive decomposition"
    - "interpretable intuitive physics model"
    - "unsupervised intuitive physics from visual observations"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to represent mechanics via long term extrapolation and interpolation"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-4 (induced by source paper "dispvoxnets non rigid point set alignment with supervised learning proxies"):
  with members:
    - "demea deep mesh autoencoders for non rigidly deforming objects"
    - "alignet partial shape agnostic alignment via unsupervised learning"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "learning free form deformations for 3d object reconstruction"
    - "deformnet free form deformation network for 3d shape reconstruction from a single image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "octnet learning deep 3d representations at high resolutions"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "flexible neural representation for physics prediction"):
  with members:
    - "probing physics knowledge using tools from developmental psychology"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "learning to play with intrinsically motivated self aware agents"
    - "visual interaction networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "neural allocentric intuitive physics prediction from real videos"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "learning physical intuition of block towers by example"

Connected through hyperedge HE-7 (induced by source paper "perceiving physical equation by observing visual scenarios"):
  with members:
    - "learning a physical long term predictor"
    - "the something something video database for learning and evaluating visual common sense"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "building machines that learn and think like people"
    - "learning physical intuition of block towers by example"
    - "a compositional object based approach to learning physical dynamics"
    - "newtonian image understanding unfolding the dynamics of objects in static images"

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)