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: "sparse topical coding"
  Abstract: We present sparse topical coding (STC), a non-probabilistic formulation of topic models for discovering latent representations of large collections of data. Unlike probabilistic topic models, STC relaxes the normalization constraint of admixture proportions and the constraint of defining a normalized likelihood function. Such relaxations make STC amenable to: 1) directly control the sparsity of inferred representations by using sparsity-inducing regularizers; 2) be seamlessly integrated with a convex error function (e.g., SVM hinge loss) for supervised learning; and 3) be efficiently learned with a simply structured coordinate descent algorithm. Our results demonstrate the advantages of STC and supervised MedSTC on identifying topical meanings of words and improving classification accuracy and time efficiency.

Connected through hyperedge HE-1 (induced by source paper "learning document embeddings along with their uncertainties"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "scdv sparse composite document vectors using soft clustering over distributional representations"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "sparsemax and relaxed wasserstein for topic sparsity"):
  with members:
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "discovering discrete latent topics with neural variational inference"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "neural variational inference for text processing"
    - "neural variational inference and learning in belief networks"
    - "probabilistic latent semantic analysis"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-3 (induced by source paper "recovery guarantee of non negative matrix factorization via alternating updates"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "on some provably correct cases of variational inference for topic models"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-4 (induced by source paper "sparse overcomplete word vector representations"):
  with members:
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "retrofitting word vectors to semantic lexicons"
    - "learning word representations with hierarchical sparse coding"
    - "efficient estimation of word representations in vector space"
    - "representation learning a review and new perspectives"
    - "from frequency to meaning vector space models of semantics"
    - "non negative sparse coding"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-5 (induced by source paper "sparse and spurious dictionary learning with noise and outliers"):
  with members:
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "local identification of overcomplete dictionaries"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "on the sample complexity of predictive sparse coding"
    - "exact recovery of sparsely used dictionaries"
    - "optimization with sparsity inducing penalties"
    - "on the local correctness of l 1 minimization for dictionary learning"

Connected through hyperedge HE-6 (induced by source paper "a poisson convolution model for characterizing topical content with word frequency and exclusivity"):
  with members:
    - "concise comparative summaries ccs of large text corpora with a human experiment"

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)