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: "a comparative evaluation of approximate probabilistic simulation and deep neural networks as accounts of human physical scene understanding"
  Abstract: Humans demonstrate remarkable abilities to predict physical events in complex scenes. Two classes of models for physical scene understanding have recently been proposed: "Intuitive Physics Engines", or IPEs, which posit that people make predictions by running approximate probabilistic simulations in causal mental models similar in nature to video-game physics engines, and memory-based models, which make judgments based on analogies to stored experiences of previously encountered scenes and physical outcomes. Versions of the latter have recently been instantiated in convolutional neural network (CNN) architectures. Here we report four experiments that, to our knowledge, are the first rigorous comparisons of simulation-based and CNN-based models, where both approaches are concretely instantiated in algorithms that can run on raw image inputs and produce as outputs physical judgments such as whether a stack of blocks will fall. Both approaches can achieve super-human accuracy levels and can quantitatively predict human judgments to a similar degree, but only the simulation-based models generalize to novel situations in ways that people do, and are qualitatively consistent with systematic perceptual illusions and judgment asymmetries that people show.

Connected through hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence"):
  with members:
    - "memo a deep network for flexible combination of episodic memories"
    - "towards a human like open domain chatbot"
    - "learning higher order logic programs"
    - "solving rubik s cube with a robot hand"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "on the measure of intelligence"
    - "entity abstraction in visual model based reinforcement learning"
    - "emergent systematic generalization in a situated agent"

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"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "chainqueen a real time differentiable physical simulator for soft robotics"

Connected through hyperedge HE-3 (induced by source paper "phyre a new benchmark for physical reasoning"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "the tools challenge rapid trial and error learning in physical problem solving"
    - "habitat a platform for embodied ai research"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "probing physics knowledge using tools from developmental psychology"
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "prediction under uncertainty with error encoding networks"
    - "shapestacks learning vision based physical intuition for generalised object stacking"

Connected through hyperedge HE-4 (induced by source paper "learning generalizable physical dynamics of 3d rigid objects"):
  with members:
    - "intphys a framework and benchmark for visual intuitive physics reasoning"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "intphys a framework and benchmark for visual intuitive physics reasoning"):
  with members:
    - "probing physics knowledge using tools from developmental psychology"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "unsupervised learning for physical interaction through video prediction"
    - "a novel performance evaluation methodology for single target trackers"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "to fall or not to fall a visual approach to physical stability prediction"

Connected through hyperedge HE-6 (induced by source paper "interpretable intuitive physics model"):
  with members:
    - "visual interaction networks"
    - "unsupervised learning for physical interaction through video prediction"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "learning physical intuition of block towers by example"
    - "the curious robot learning visual representations via physical interactions"
    - "what happens if learning to predict the effect of forces in images"

Connected through hyperedge HE-7 (induced by source paper "information theoretic model identification and policy search using physics engines with application to robotic manipulation"):
  with members:
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "deep visual foresight for planning robot motion"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "a convex polynomial force motion model for planar sliding identification and application"
    - "learning physical intuition of block towers by example"
    - "attend infer repeat fast scene understanding with generative models"
    - "the curious robot learning visual representations via physical interactions"

Connected through hyperedge HE-8 (induced by source paper "learning physical intuition of block towers by example"):
  with members:
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning to segment object candidates"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to see by moving"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

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.AI(Artificial Intelligence)