human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "anchored correlation explanation topic modeling with minimal domain knowledge"):
  Abstract: While generative models such as Latent Dirichlet Allocation (LDA) have proven fruitful in topic modeling, they often require detailed assumptions and careful specification of hyperparameters. Such model complexity issues only compound when trying to generalize generative models to incorporate human input. We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. This framework naturally generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions. In particular, word-level domain knowledge can be flexibly incorporated within CorEx through anchor words, allowing topic separability and representation to be promoted with minimal human intervention. Across a variety of datasets, metrics, and experiments, we demonstrate that CorEx produces topics that are comparable in quality to those produced by unsupervised and semi-supervised variants of LDA.

Includes members:
  Member "progressive em for latent tree models and hierarchical topic detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conformative filtering for implicit feedback data")
  Member "discovering structure in high dimensional data through correlation explanation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correlation congruence for knowledge distillation")
  Member "probabilistic latent semantic analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two level transformer and auxiliary coherence modeling for improved text segmentation")
  Member "a practical algorithm for topic modeling with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training")
  Member "scikit learn machine learning in python":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scail classifier weights scaling for class incremental learning")
  Member "the author topic model for authors and documents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "learning topic models going beyond svd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "contrastive estimation reveals topic posterior information to linear models")

Question: Please tell me which class the source paper 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.CL(Computation and Language)