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: "unsupervised deep haar scattering on graphs"
  Abstract: The classification of high-dimensional data defined on graphs is particularly difficult when the graph geometry is unknown. We introduce a Haar scattering transform on graphs, which computes invariant signal descriptors. It is implemented with a deep cascade of additions, subtractions and absolute values, which iteratively compute orthogonal Haar wavelet transforms. Multiscale neighborhoods of unknown graphs are estimated by minimizing an average total variation, with a pair matching algorithm of polynomial complexity. Supervised classification with dimension reduction is tested on data bases of scrambled images, and for signals sampled on unknown irregular grids on a sphere.

Connected through hyperedge HE-1 (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"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "graph based convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "a weakly supervised learning based clustering framework"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "a compact network learning model for distribution regression"
    - "predicting time varying distributions with limited training data"
    - "deep sets"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deepsetnet predicting sets with deep neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "graph fourier transform based on ell_1 norm variation minimization"):
  with members:
    - "discrete signal processing on graphs sampling theory"
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-4 (induced by source paper "adversarial data programming using gans to relax the bottleneck of curated labeled data"):
  with members:
    - "toward controlled generation of text"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "discrete continuous admm for transductive inference in higher order mrfs"
    - "triangle generative adversarial networks"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "triple generative adversarial nets"
    - "inferring generative model structure with static analysis"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "classifying unordered feature sets with convolutional deep averaging networks"):
  with members:
    - "a bag of words equivalent recurrent neural network for action recognition"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "learning distributed representations of sentences from unlabelled data"
    - "group equivariant convolutional networks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "deep residual learning for image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "underwater object classification using scattering transform of sonar signals"):
  with members:
    - "discrete deep feature extraction a theory and new architectures"
    - "deep convolutional neural networks based on semi discrete frames"

Connected through hyperedge HE-7 (induced by source paper "convolutional neural networks on graphs with fast localized spectral filtering"):
  with members:
    - "toward an uncertainty principle for weighted graphs"
    - "deep convolutional networks on graph structured data"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "spatial transformer networks"
    - "on the degrees of freedom of signals on graphs"
    - "adam a method for stochastic optimization"
    - "a multiscale pyramid transform for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-8 (induced by source paper "machine olfaction using time scattering of sensor multiresolution graphs"):
  with members:
    - "classification with scattering operators"

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)