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: "theory guided data science a new paradigm for scientific discovery from data"
  Abstract: Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of data science models in enabling scientific discovery. The overarching vision of TGDS is to introduce scientific consistency as an essential component for learning generalizable models. Further, by producing scientifically interpretable models, TGDS aims to advance our scientific understanding by discovering novel domain insights. Indeed, the paradigm of TGDS has started to gain prominence in a number of scientific disciplines such as turbulence modeling, material discovery, quantum chemistry, bio-medical science, bio-marker discovery, climate science, and hydrology. In this paper, we formally conceptualize the paradigm of TGDS and present a taxonomy of research themes in TGDS. We describe several approaches for integrating domain knowledge in different research themes using illustrative examples from different disciplines. We also highlight some of the promising avenues of novel research for realizing the full potential of theory-guided data science.

Connected through hyperedge HE-1 (induced by source paper "from data to actions in intelligent transportation systems a prescription of functional requirements for model actionability"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "predicting transportation modes of gps trajectories using feature engineering and noise removal"
    - "explanation methods in deep learning users values concerns and challenges"
    - "transferable pedestrian motion prediction models at intersections"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "understanding traffic density from large scale web camera data"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"

Connected through hyperedge HE-2 (induced by source paper "using deep learning to extend the range of air pollution monitoring and forecasting"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "fd net with auxiliary time steps fast prediction of pdes using hessian free trust region methods"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "deep air learning interpolation prediction and feature analysis of fine grained air quality"
    - "machine learning of linear differential equations using gaussian processes"

Connected through hyperedge HE-3 (induced by source paper "combining parametric land surface models with machine learning"):
  with members:
    - "towards learning universal regional and local hydrological behaviors via machine learning applied to large sample datasets"

Connected through hyperedge HE-4 (induced by source paper "explainable machine learning for scientific insights and discoveries"):
  with members:
    - "conditioning by adaptive sampling for robust design"
    - "a rate distortion framework for explaining neural network decisions"
    - "explaining explanations in ai"
    - "neural guided symbolic regression with semantic prior"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "causality for machine learning"
    - "interpretations are useful penalizing explanations to align neural networks with prior knowledge"
    - "the challenge of crafting intelligible intelligence"

Connected through hyperedge HE-5 (induced by source paper "interpreting recurrent neural networks behaviour via excitable network attractors"):
  with members:
    - "full force a target based method for training recurrent networks"
    - "can recurrent neural networks warp time"
    - "methods for interpreting and understanding deep neural networks"
    - "training echo state networks with regularization through dimensionality reduction"
    - "orthogonal echo state networks and stochastic evaluations of likelihoods"
    - "an overview of gradient descent optimization algorithms"
    - "unitary evolution recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "deep learning of subsurface flow via theory guided neural network"):
  with members:
    - "deep learning based automatic video annotation tool for self driving car"
    - "lanenet real time lane detection networks for autonomous driving"
    - "physics guided neural networks pgnn an application in lake temperature modeling"
    - "tensorflow a system for large scale machine learning"
    - "automatic differentiation in machine learning a survey"
    - "a primer on neural network models for natural language processing"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "physics guided architecture pga of neural networks for quantifying uncertainty in lake temperature modeling"):
  with members:
    - "imposing hard constraints on deep networks promises and limitations"
    - "bayesian recurrent neural networks"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "physics guided neural networks pgnn an application in lake temperature modeling"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-8 (induced by source paper "visualizing the consequences of climate change using cycle consistent adversarial networks"):
  with members:
    - "exascale deep learning for climate analytics"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "machine learning for the geosciences challenges and opportunities"
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
    - "deep residual learning for image recognition"
    - "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.LG(Machine Learning)