human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "output space search for structured prediction"
  Abstract: We consider a framework for structured prediction based on search in the space of complete structured outputs. Given a structured input, an output is produced by running a time-bounded search procedure guided by a learned cost function, and then returning the least cost output uncovered during the search. This framework can be instantiated for a wide range of search spaces and search procedures, and easily incorporates arbitrary structured-prediction loss functions. In this paper, we make two main technical contributions. First, we define the limited-discrepancy search space over structured outputs, which is able to leverage powerful classification learning algorithms to improve the search space quality. Second, we give a generic cost function learning approach, where the key idea is to learn a cost function that attempts to mimic the behavior of conducting searches guided by the true loss function. Our experiments on six benchmark domains demonstrate that using our framework with only a small amount of search is sufficient for significantly improving on state-of-the-art structured-prediction performance.

Connected through hyperedge HE-1 (induced by source paper "rectifying classifier chains for multi label classification"):
  with members:
    - "search based structured prediction"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-2 (induced by source paper "learning reductions that really work"):
  with members:
    - "learning to search better than your teacher"
    - "logarithmic time online multiclass prediction"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "efficient programmable learning to search"
    - "least squares revisited scalable approaches for multi class prediction"
    - "normalized online learning"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"
    - "online importance weight aware updates"

Connected through hyperedge HE-3 (induced by source paper "learning to search for dependencies"):
  with members:
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "efficient programmable learning to search"
    - "normalized online learning"
    - "online importance weight aware updates"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "adaptive bound optimization for online convex optimization"
    - "search based structured prediction"

Connected through hyperedge HE-4 (induced by source paper "parser for abstract meaning representation using learning to search"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "efficient programmable learning to search"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"
    - "learning as search optimization approximate large margin methods for structured prediction"

Connected through hyperedge HE-5 (induced by source paper "efficient programmable learning to search"):
  with members:
    - "transition based dependency parsing with stack long short term memory"
    - "learning to search for dependencies"
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "pegasus a policy search method for large mdps and pomdps"
    - "normalized online learning"
    - "church a language for generative models"
    - "a reliable effective terascale linear learning system"

Question: Please tell me which class the center node 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)