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: "scipy 1 0 fundamental algorithms for scientific computing in python"
  Abstract: SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

Connected through hyperedge HE-1 (induced by source paper "learning to segment 3d point clouds in 2d image space"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "mint deep network compression via mutual information based neuron trimming"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to see the wood for the trees deep laser localization in urban and natural environments on a cpu"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "geometric estimation of multivariate dependency"
    - "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"

Connected through hyperedge HE-3 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "the case for bayesian deep learning"
    - "fantastic generalization measures and where to find them"
    - "output constrained bayesian neural networks"
    - "large scale structure of neural network loss landscapes"
    - "functional variational bayesian neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-4 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy"):
  with members:
    - "graphical model based estimation and inference for differential privacy"
    - "the power of factorization mechanisms in local and central differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "linear queries estimation with local differential privacy"
    - "ektelo a framework for defining differentially private computations"
    - "practical locally private heavy hitters"

Connected through hyperedge HE-5 (induced by source paper "an api oriented open source python framework for unsupervised learning on graphs"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "multi scale attributed node embedding"
    - "edmot an edge enhancement approach for motif aware community detection"
    - "pytorch an imperative style high performance deep learning library"
    - "netlsd hearing the shape of a graph"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a simple baseline algorithm for graph classification"
    - "learning structural node embeddings via diffusion wavelets"

Connected through hyperedge HE-6 (induced by source paper "disentangling image distortions in deep feature space"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "perceptual losses for real time style transfer and super resolution"
    - "what makes imagenet good for transfer learning"
    - "understanding neural networks through deep visualization"
    - "a neural algorithm of artistic style"
    - "deep residual learning for image recognition"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "a utility based analysis of equilibria in multi objective normal form games"):
  with members:
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "multi objective deep reinforcement learning"
    - "structure in the value function of two player zero sum games of incomplete information"
    - "a survey of multi objective sequential decision making"

Connected through hyperedge HE-8 (induced by source paper "deep learning of dynamical attractors from time series measurements"):
  with members:
    - "learning low dimensional state embeddings and metastable clusters from time series data"
    - "learning koopman invariant subspaces for dynamic mode decomposition"
    - "persistent homology of attractors for action recognition"
    - "reducing overfitting in deep networks by decorrelating representations"

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.MS(Mathematical Software)