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: "combination strategies for semantic role labeling"
  Abstract: This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful - they all outperform the current best results reported in the CoNLL-2005 evaluation exercise - but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback.

Connected through hyperedge HE-1 (induced by source paper "revisiting joint modeling of cross document entity and event coreference resolution"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "higher order coreference resolution with coarse to fine inference"
    - "end to end neural coreference resolution"
    - "event coreference resolution by iteratively unfolding inter dependencies among events"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "improving coreference resolution by learning entity level distributed representations"
    - "a hierarchical distance dependent bayesian model for event coreference resolution"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "linguistically informed self attention for semantic role labeling"):
  with members:
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "learning structured text representations"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"

Connected through hyperedge HE-3 (induced by source paper "syntax helps elmo understand semantics is syntax still relevant in a deep neural architecture for srl"):
  with members:
    - "deep contextualized word representations"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "end to end neural coreference resolution"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "semi supervised sequence tagging with bidirectional language models"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-4 (induced by source paper "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"):
  with members:
    - "enriching word vectors with subword information"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "neural semantic role labeling with dependency path embeddings"
    - "incremental parsing with minimal features using bi directional lstm"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "transition based dependency parsing with stack long short term memory"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-5 (induced by source paper "event coreference resolution by iteratively unfolding inter dependencies among events"):
  with members:
    - "improving coreference resolution by learning entity level distributed representations"
    - "learning global features for coreference resolution"
    - "a hierarchical distance dependent bayesian model for event coreference resolution"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-6 (induced by source paper "a hierarchical distance dependent bayesian model for event coreference resolution"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distantly labeling data for large scale cross document coreference"

Connected through hyperedge HE-7 (induced by source paper "integrative semantic dependency parsing via efficient large scale feature selection"):
  with members:
    - "improving the accuracy and efficiency of map inference for markov logic"

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.AI(Artificial Intelligence)