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: "learning equations for extrapolation and control"
  Abstract: We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.

Connected through hyperedge HE-1 (induced by source paper "out of distribution generalization via risk extrapolation rex"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "achieving robustness in the wild via adversarial mixing with disentangled representations"
    - "domain generalization by solving jigsaw puzzles"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "do imagenet classifiers generalize to imagenet"
    - "a closer look at few shot classification"
    - "a fourier perspective on model robustness in computer vision"

Connected through hyperedge HE-2 (induced by source paper "visual physics discovering physical laws from videos"):
  with members:
    - "data driven partial derivative equations discovery with evolutionary approach"
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "physics based rendering for improving robustness to rain"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "neural guided symbolic regression with semantic prior"
    - "physics based neural networks for shape from polarization"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "neural lander stable drone landing control using learned dynamics"

Connected through hyperedge HE-3 (induced by source paper "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "extracting structured dynamical systems using sparse optimization with very few samples"
    - "stable architectures for deep neural networks"
    - "model reduction with memory and the machine learning of dynamical systems"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"

Connected through hyperedge HE-4 (induced by source paper "deep symbolic regression recovering mathematical expressions from data via risk seeking policy gradients"):
  with members:
    - "neural program synthesis with priority queue training"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "attention is all you need"
    - "searching for activation functions"
    - "neural optimizer search with reinforcement learning"
    - "swish a self gated activation function"

Connected through hyperedge HE-5 (induced by source paper "integration of neural network based symbolic regression in deep learning for scientific discovery"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "the state of sparsity in deep neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "neural arithmetic logic units"
    - "extrapolation and learning equations"
    - "training sparse neural networks"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-6 (induced by source paper "neural guided symbolic regression with asymptotic constraints"):
  with members:
    - "neural guided deductive search for real time program synthesis from examples"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "robustfill neural program learning under noisy i o"
    - "deepcoder learning to write programs"
    - "neuro symbolic program synthesis"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "neural guided symbolic regression with semantic prior"):
  with members:
    - "neural guided deductive search for real time program synthesis from examples"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "searching for activation functions"
    - "neuro symbolic program synthesis"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "data driven modelling of dynamical systems using tree adjoining grammar and genetic programming"):
  with members:
    - "structure selection of polynomial narx models using two dimensional 2d particle swarms"
    - "on the simulation of polynomial narmax models"
    - "grammar based representation and identification of dynamical systems"
    - "empirical modeling using genetic programming a survey of issues and approaches"

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.LG(Machine Learning)