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 semantic script knowledge with event embeddings"
  Abstract: Induction of common sense knowledge about prototypical sequences of events has recently received much attention. Instead of inducing this knowledge in the form of graphs, as in much of the previous work, in our method, distributed representations of event realizations are computed based on distributed representations of predicates and their arguments, and then these representations are used to predict prototypical event orderings. The parameters of the compositional process for computing the event representations and the ranking component of the model are jointly estimated from texts. We show that this approach results in a substantial boost in ordering performance with respect to previous methods.

Connected through hyperedge HE-1 (induced by source paper "event representation learning enhanced with external commonsense knowledge"):
  with members:
    - "story ending prediction by transferable bert"
    - "deep contextualized word representations"
    - "constructing narrative event evolutionary graph for script event prediction"
    - "event representations with tensor based compositions"
    - "deep biaffine attention for neural dependency parsing"
    - "a study of entanglement in a categorical framework of natural language"
    - "predicting abnormal returns from news using text classification"

Connected through hyperedge HE-2 (induced by source paper "mcscript a novel dataset for assessing machine comprehension using script knowledge"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "inscript narrative texts annotated with script information"
    - "newsqa a machine comprehension dataset"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "squad 100 000 questions for machine comprehension of text"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-3 (induced by source paper "event representations with tensor based compositions"):
  with members:
    - "zero shot transfer learning for event extraction"
    - "distributed representations of sentences and documents"
    - "a study of entanglement in a categorical framework of natural language"
    - "evaluating neural word representations in tensor based compositional settings"
    - "probabilistic frame induction"
    - "multi step regression learning for compositional distributional semantics"

Connected through hyperedge HE-4 (induced by source paper "automated prediction of temporal relations"):
  with members:
    - "aggressive actions and anger detection from multiple modalities using kinect"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "xml entity architecture for efficient software integration"
    - "embracing agile methodology during devops developer internship program"
    - "affect intensity estimation using multiple modalities"
    - "learning sentence internal temporal relations"

Connected through hyperedge HE-5 (induced by source paper "two discourse driven language models for semantics"):
  with members:
    - "solving hard coreference problems"
    - "unsupervised induction of semantic roles within a reconstruction error minimization framework"
    - "probabilistic frame induction"
    - "efficient estimation of word representations in vector space"

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)