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: "gurls a least squares library for supervised learning"
  Abstract: We present GURLS, a least squares, modular, easy-to-extend software library for efficient supervised learning. GURLS is targeted to machine learning practitioners, as well as non-specialists. It offers a number state-of-the-art training strategies for medium and large-scale learning, and routines for efficient model selection. The library is particularly well suited for multi-output problems (multi-category/multi-label). GURLS is currently available in two independent implementations: Matlab and C++. It takes advantage of the favorable properties of regularized least squares algorithm to exploit advanced tools in linear algebra. Routines to handle computations with very large matrices by means of memory-mapped storage and distributed task execution are available. The package is distributed under the BSD licence and is available for download at this https URL

Connected through hyperedge HE-1 (induced by source paper "offensive language detection a comparative analysis"):
  with members:
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "large scale kernel methods and applications to lifelong robot learning"):
  with members:
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "on the equivalence between quadrature rules and random features"
    - "train faster generalize better stability of stochastic gradient descent"
    - "multimodal deep learning for robust rgb d object recognition"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "efficient algorithms and error analysis for the modified nystrom method"

Connected through hyperedge HE-3 (induced by source paper "controlled tactile exploration and haptic object recognition"):
  with members:
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"

Connected through hyperedge HE-4 (induced by source paper "real world object recognition with off the shelf deep conv nets how many objects can icub learn"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "learning with group invariant features a kernel perspective"):
  with members:
    - "on the equivalence between quadrature rules and random features"
    - "convolutional kernel networks"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "invariant scattering convolution networks"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "random maxout features"):
  with members:
    - "on the equivalence between quadrature rules and random features"

Connected through hyperedge HE-7 (induced by source paper "a deep representation for invariance and music classification"):
  with members:
    - "deep scattering spectrum"
    - "deep learning by scattering"
    - "unsupervised learning of invariant representations in hierarchical architectures"

Connected through hyperedge HE-8 (induced by source paper "learning an invariant speech representation"):
  with members:
    - "deep scattering spectrum"
    - "unsupervised learning of invariant representations in hierarchical architectures"
    - "feature learning in deep neural networks studies on speech recognition tasks"

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)