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 "prediction focused topic models for electronic health records"):
  Abstract: Electronic Health Record (EHR) data can be represented as discrete counts over a high dimensional set of possible procedures, diagnoses, and medications. Supervised topic models present an attractive option for incorporating EHR data as features into a prediction problem: given a patient's record, we estimate a set of latent factors that are predictive of the response variable. However, existing methods for supervised topic modeling struggle to balance prediction quality and coherence of the latent factors. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only features that improve, or do not hinder, prediction performance. By removing features with irrelevant signal, the topic model is able to learn task-relevant, interpretable topics. We demonstrate on a EHR dataset and a movie review dataset that compared to existing approaches, prediction-focused topic models are able to learn much more coherent topics while maintaining competitive predictions.

Includes members:
  Member "prior matters simple and general methods for evaluating and improving topic quality in topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prediction focused topic models via feature selection")
  Member "prediction constrained topic models for antidepressant recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prediction focused topic models via feature selection")
  Member "crime topic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prediction focused topic models via feature selection")
  Member "end to end learning of lda by mirror descent back propagation over a deep architecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cooperative neural networks conn exploiting prior independence structure for improved classification")
  Member "variable selection for latent dirichlet allocation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prediction focused topic models via feature selection")
  Member "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word 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.LG(Machine Learning)