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: "learning as search optimization approximate large margin methods for structured prediction"
  Abstract: Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare that exact search or parameter estimation is tractable. Instead of learning exact models and searching via heuristic means, we embrace this difficulty and treat the structured output problem in terms of approximate search. We present a framework for learning as search optimization, and two parameter updates with convergence theorems and bounds. Empirical evidence shows that our integrated approach to learning and decoding can outperform exact models at smaller computational cost.

Connected through hyperedge HE-1 (induced by source paper "rewarding smatch transition based amr parsing with reinforcement learning"):
  with members:
    - "amr parsing as sequence to graph transduction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an amr aligner tuned by transition based parser"
    - "amr parsing as graph prediction with latent alignment"
    - "neural semantic parsing by character based translation experiments with abstract meaning representations"
    - "amr parsing using stack lstms"
    - "weakly supervised cross lingual named entity recognition via effective annotation and representation projection"

Connected through hyperedge HE-2 (induced by source paper "a fully differentiable beam search decoder"):
  with members:
    - "fully convolutional speech recognition"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "improving end to end speech recognition with policy learning"
    - "wav2letter an end to end convnet based speech recognition system"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "a continuous relaxation of beam search for end to end training of neural sequence models"
    - "cold fusion training seq2seq models together with language models"

Connected through hyperedge HE-3 (induced by source paper "worst case polynomial time exact map inference on discrete models with global dependencies"):
  with members:
    - "optimizing for measure of performance in max margin parsing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a compact linear programming relaxation for binary sub modular mrf"
    - "tighter linear program relaxations for high order graphical models"
    - "discriminative probabilistic models for relational data"
    - "fast exact inference for recursive cardinality models"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "tightening lp relaxations for map using message passing"

Connected through hyperedge HE-4 (induced by source paper "optimal completion distillation for sequence learning"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "token level and sequence level loss smoothing for rnn language models"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "convergence of value aggregation for imitation learning"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "improved training of end to end attention models for speech recognition"

Connected through hyperedge HE-5 (induced by source paper "from credit assignment to entropy regularization two new algorithms for neural sequence prediction"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "softmax q distribution estimation for structured prediction a theoretical interpretation for raml"
    - "equivalence between policy gradients and soft q learning"
    - "learning to decode for future success"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "sequence to sequence learning as beam search optimization"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "policy shaping and generalized update equations for semantic parsing from denotations"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "deep reinforcement learning for dialogue generation"
    - "global neural ccg parsing with optimality guarantees"
    - "data recombination for neural semantic parsing"
    - "deep reinforcement learning for mention ranking coreference models"
    - "improving information extraction by acquiring external evidence with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "collective entity disambiguation with structured gradient tree boosting"):
  with members:
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "globally normalized transition based neural networks"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"
    - "probabilistic bag of hyperlinks model for entity linking"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-8 (induced by source paper "online markov decoding lower bounds and near optimal approximation algorithms"):
  with members:
    - "learning small predictors"
    - "improving viterbi is hard better runtimes imply faster clique algorithms"
    - "decoding hidden markov models faster than viterbi via online matrix vector max multiplication"
    - "on line viterbi algorithm for analysis of long biological sequences"

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)