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: "long term image boundary prediction"
  Abstract: Boundary estimation in images and videos has been a very active topic of research, and organizing visual information into boundaries and segments is believed to be a corner stone of visual perception. While prior work has focused on estimating boundaries for observed frames, our work aims at predicting boundaries of future unobserved frames. This requires our model to learn about the fate of boundaries and corresponding motion patterns -- including a notion of "intuitive physics". We experiment on natural video sequences along with synthetic sequences with deterministic physics-based and agent-based motions. While not being our primary goal, we also show that fusion of RGB and boundary prediction leads to improved RGB predictions.

Connected through hyperedge HE-1 (induced by source paper "making sense of sensory input"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "model based reinforcement learning for atari"
    - "unsupervised discovery of parts structure and dynamics"
    - "composable planning with attributes"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "graph networks as learnable physics engines for inference and control"
    - "semi supervised online structure learning for composite event recognition"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-2 (induced by source paper "structured agents for physical construction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "composable planning with attributes"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "surprising negative results for generative adversarial tree search"
    - "graph networks as learnable physics engines for inference and control"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "unity a general platform for intelligent agents"
    - "shapestacks learning vision based physical intuition for generalised object stacking"

Connected through hyperedge HE-3 (induced by source paper "unsupervised intuitive physics from past experiences"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "neural allocentric intuitive physics prediction from real videos"
    - "graph networks as learnable physics engines for inference and control"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "unsupervised intuitive physics from visual observations"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "recurrent environment simulators"

Connected through hyperedge HE-4 (induced by source paper "unsupervised intuitive physics from visual observations"):
  with members:
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "graph networks as learnable physics engines for inference and control"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "stochastic adversarial video prediction"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "predicting deeper into the future of semantic segmentation"
    - "learning a physical long term predictor"
    - "learning to represent mechanics via long term extrapolation and interpolation"

Connected through hyperedge HE-5 (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"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "acquiring target stacking skills by goal parameterized deep reinforcement learning"):
  with members:
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "learning physical intuition of block towers by example"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "learning visual predictive models of physics for playing billiards"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-7 (induced by source paper "visual interaction networks"):
  with members:
    - "learning a physical long term predictor"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "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"
    - "learning visual predictive models of physics for playing billiards"

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)