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 "minimax bounds for structured prediction"):
  Abstract: Structured prediction can be considered as a generalization of many standard supervised learning tasks, and is usually thought as a simultaneous prediction of multiple labels. One standard approach is to maximize a score function on the space of labels, which decomposes as a sum of unary and pairwise potentials, each depending on one or two specific labels, respectively. For this approach, several learning and inference algorithms have been proposed over the years, ranging from exact to approximate methods while balancing the computational complexity. However, in contrast to binary and multiclass classification, results on the necessary number of samples for achieving learning is still limited, even for a specific family of predictors such as factor graphs. In this work, we provide minimax bounds for a class of factor-graph inference models for structured prediction. That is, we characterize the necessary sample complexity for any conceivable algorithm to achieve learning of factor-graph predictors.

Includes members:
  Member "learning maximum a posteriori perturbation models for structured prediction in polynomial time":
    also connected through other hyperedges:
      (none)
  Member "learning latent variable structured prediction models with gaussian perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exact inference in structured prediction")
  Member "on the information theoretic limits of learning ising models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the sample complexity of meta sparse regression")
  Member "grammar as a foreign language":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parsing as pretraining")
  Member "show and tell a neural image caption generator":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "multiclass learnability and the erm principle":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "search based structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "loss annealed gail for sample efficient and stable imitation learning")
  Member "information theoretic limits of selecting binary graphical models in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privately learning markov random fields")

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)