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: "physics guided neural networks pgnn an application in lake temperature modeling"
  Abstract: This paper introduces a novel framework for learning data science models by using the scientific knowledge encoded in physics-based models. This framework, termed as physics-guided neural network (PGNN), leverages the output of physics-based model simulations along with observational features to generate predictions using a neural network architecture. Further, we present a novel class of learning objective for training neural networks, which ensures that the model predictions not only show lower errors on the training data but are also \emph{consistent} with the known physics. We illustrate the effectiveness of PGNN for the problem of lake temperature modeling, where physical relationships between the temperature, density, and depth of water are used in the learning of neural network model parameters. By using scientific knowledge to guide the construction and learning of neural networks, we are able to show that the proposed framework ensures better generalizability as well as physical consistency of results.

Connected through hyperedge HE-1 (induced by source paper "physics guided machine learning for scientific discovery an application in simulating lake temperature profiles"):
  with members:
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "a domain guided cnn architecture for predicting age from structural brain images"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "physics based generative adversarial models for image restoration and beyond"
    - "label free supervision of neural networks with physics and domain knowledge"

Connected through hyperedge HE-2 (induced by source paper "blending diverse physical priors with neural networks"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "neural illumination lighting prediction for indoor environments"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "block coordinate regularization by denoising"
    - "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"
    - "deep learning approach for fourier ptychography microscopy"

Connected through hyperedge HE-3 (induced by source paper "generalizing deep models for overhead image segmentation through getis ord gi pooling"):
  with members:
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "physics based neural networks for shape from polarization"):
  with members:
    - "neural lander stable drone landing control using learned dynamics"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "inverse transport networks"
    - "learning a discriminative prior for blind image deblurring"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "single image svbrdf capture with a rendering aware deep network"
    - "flash photography for data driven hidden scene recovery"

Connected through hyperedge HE-5 (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"
    - "theory guided data science a new paradigm for scientific discovery from data"
    - "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-6 (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"
    - "theory guided data science a new paradigm for scientific discovery from data"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-7 (induced by source paper "an iterative scientific machine learning approach for discovery of theories underlying physical phenomena"):
  with members:
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "theory guided data science a new paradigm for scientific discovery from data"

Connected through hyperedge HE-8 (induced by source paper "opening the black box of deep learning"):
  with members:
    - "decoupled networks"
    - "a mathematical framework for superintelligent machines"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "what do we understand about convolutional networks"
    - "large scale distributed neural network training through online distillation"
    - "deep learning a critical appraisal"
    - "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
    - "mathematics of deep learning"

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)