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 "input output deep architecture for structured output problems"):
  Abstract: Pre-training of input layers has shown to be efficient for learning deep architectures, solving the vanishing gradient issues. In this paper, we propose to extend the use of pre-training to output layers in order to address structured output problems, which are characterized by internal dependencies between the outputs (e.g. the classes of pixels in an image labeling problem). Whereas the output structure is generally modeled using graphical models, we propose a fully neural-based model called IODA (Input Output Deep Architecture) that learns both input and output dependencies. We apply IODA on facial landmark detection problems where the output is a strongly structured regression vector. We evaluate IODA on two public challenging datasets: LFPW and HELEN. We show that IODA outperforms the traditional pre-training approach.

Includes members:
  Member "learning hidden markov models for regression using path aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "facial landmark detection using structured output deep neural networks")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nestedvae isolating common factors via weak supervision")

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)