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: "intphys a framework and benchmark for visual intuitive physics reasoning"
  Abstract: In order to reach human performance on complex visual tasks, artificial systems need to incorporate a significant amount of understanding of the world in terms of macroscopic objects, movements, forces, etc. Inspired by work on intuitive physics in infants, we propose an evaluation framework which diagnoses how much a given system understands about physics by testing whether it can tell apart well matched videos of possible versus impossible events. The test requires systems to compute a physical plausibility score over an entire video. It is free of bias and can test a range of specific physical reasoning skills. We then describe the first release of a benchmark dataset aimed at learning intuitive physics in an unsupervised way, using videos constructed with a game engine. We describe two Deep Neural Network baseline systems trained with a future frame prediction objective and tested on the possible versus impossible discrimination task. The analysis of their results compared to human data gives novel insights in the potentials and limitations of next frame prediction architectures.

Connected through hyperedge HE-1 (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"
    - "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"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-2 (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"
    - "prediction under uncertainty with error encoding networks"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "building generalizable agents with a realistic and rich 3d environment"

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"
    - "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"
    - "neural expectation maximization"

Connected through hyperedge HE-4 (induced by source paper "cophy counterfactual learning of physical dynamics"):
  with members:
    - "unsupervised intuitive physics from past experiences"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "phyre a new benchmark for physical reasoning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "shapestacks learning vision based physical intuition for generalised object stacking"

Connected through hyperedge HE-5 (induced by source paper "learning generalizable physical dynamics of 3d rigid objects"):
  with members:
    - "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"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-6 (induced by source paper "flexible neural representation for physics prediction"):
  with members:
    - "probing physics knowledge using tools from developmental psychology"
    - "3d physnet learning the intuitive physics of non rigid object deformations"
    - "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-7 (induced by source paper "unsupervised intuitive physics from visual observations"):
  with members:
    - "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"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"

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)