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: "permutation equivariant neural networks applied to dynamics prediction"
  Abstract: The introduction of convolutional layers greatly advanced the performance of neural networks on image tasks due to innately capturing a way of encoding and learning translation-invariant operations, matching one of the underlying symmetries of the image domain. In comparison, there are a number of problems in which there are a number of different inputs which are all 'of the same type' --- multiple particles, multiple agents, multiple stock prices, etc. The corresponding symmetry to this is permutation symmetry, in that the algorithm should not depend on the specific ordering of the input data. We discuss a permutation-invariant neural network layer in analogy to convolutional layers, and show the ability of this architecture to learn to predict the motion of a variable number of interacting hard discs in 2D. In the same way that convolutional layers can generalize to different image sizes, the permutation layer we describe generalizes to different numbers of objects.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "discovering nonlinear relations with minimum predictive information regularization"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "mine mutual information neural estimation"
    - "vain attentional multi agent predictive modeling"
    - "the power of deeper networks for expressing natural functions"
    - "interaction networks for learning about objects relations and physics"
    - "a compositional object based approach to learning physical dynamics"
    - "adam a method for stochastic optimization"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-3 (induced by source paper "coloring graph neural networks for node disambiguation"):
  with members:
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"
    - "relational pooling for graph representations"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deep sets"
    - "invariant and equivariant graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "on incorporating semantic prior knowlegde in deep learning through embedding space constraints"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "actively seeking and learning from live data"
    - "hyperbolic neural networks"
    - "compositional attention networks for machine reasoning"
    - "deep learning a critical appraisal"
    - "visual question answering as a meta learning task"
    - "don t just assume look and answer overcoming priors for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "graph convolutional neural networks via scattering"):
  with members:
    - "encoding robust representation for graph generation"
    - "covariant compositional networks for learning graphs"
    - "dynamic routing between capsules"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "energy propagation in deep convolutional neural networks"
    - "graph based convolutional neural network"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "factorised neural relational inference for multi interaction systems"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "visual interaction networks"
    - "a simple neural network module for relational reasoning"
    - "neural message passing for quantum chemistry"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "learning multiagent communication with backpropagation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "mixed batches and symmetric discriminators for gan training"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "deep sets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "density estimation using real nvp"
    - "improved techniques for training gans"
    - "deep unsupervised learning using nonequilibrium thermodynamics"

Connected through hyperedge HE-8 (induced by source paper "a likelihood free inference framework for population genetic data using exchangeable neural networks"):
  with members:
    - "deep sets"
    - "on calibration of modern neural networks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "understanding deep learning requires rethinking generalization"
    - "adam a method for stochastic optimization"

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)