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 extrapolation"
  Abstract: Boundary prediction 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 predicting 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 extrapolate motion patterns. We experiment on established real-world video segmentation dataset, which provides a testbed for this new task. We show for the first time spatio-temporal boundary extrapolation, that in contrast to prior work on RGB extrapolation maintains a crisp result. Furthermore, we show long-term prediction of boundaries in situations where the motion is governed by the laws of physics. We argue that our model has with minimalistic model assumptions derived a notion of "intuitive physics".

Connected through hyperedge HE-1 (induced by source paper "learning manipulation under physics constraints with visual perception"):
  with members:
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "learning model based planning from scratch"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "metacontrol for adaptive imagination based optimization"
    - "see the glass half full reasoning about liquid containers their volume and content"
    - "imagination augmented agents for deep reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "learning to poke by poking experiential learning of intuitive physics"

Connected through hyperedge HE-2 (induced by source paper "answering visual what if questions from actions to predicted scene descriptions"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "dvqa understanding data visualizations via question answering"
    - "visual interaction networks"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "taking visual motion prediction to new heightfields"

Connected through hyperedge HE-3 (induced by source paper "relational inductive bias for physical construction in humans and machines"):
  with members:
    - "physical problem solving joint planning with symbolic geometric and dynamic constraints"
    - "neural message passing for quantum chemistry"
    - "metacontrol for adaptive imagination based optimization"
    - "learning combinatorial optimization algorithms over graphs"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "gated graph sequence neural networks"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "learning physical intuition of block towers by example"

Connected through hyperedge HE-4 (induced by source paper "convolutional invasion and expansion networks for tumor growth prediction"):
  with members:
    - "predicting deeper into the future of semantic segmentation"
    - "deeppap deep convolutional networks for cervical cell classification"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "unsupervised learning for physical interaction through video prediction"
    - "convolutional two stream network fusion for video action recognition"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "deep multi scale video prediction beyond mean square error"

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)