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 "search based structured prediction"):
  Abstract: We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem.

Includes members:
  Member "pegasus a policy search method for large mdps and pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control")
  Member "case factor diagrams for structured probabilistic modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an algebraic formalization of forward and forward backward algorithms")
  Member "bayesian query focused summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving the similarity measure of determinantal point processes for extractive multi document summarization")
  Member "a noisy channel model for document compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint parsing and generation for abstractive summarization")
  Member "a large scale exploration of effective global features for a joint entity detection and tracking model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward gender inclusive coreference resolution")
  Member "learning as search optimization approximate large margin methods for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rewarding smatch transition based amr parsing with reinforcement learning")
  Member "the use of classifiers in sequential inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reasoning driven question answering for natural language understanding")

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)