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: "a hierarchical distance dependent bayesian model for event coreference resolution"
  Abstract: We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions -- information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametric clustering. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution.

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"
    - "adam a method for stochastic optimization"
    - "combination strategies for semantic role labeling"

Connected through hyperedge HE-2 (induced by source paper "natural language processing for information extraction"):
  with members:
    - "neighborhood mixture model for knowledge base completion"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"
    - "classifying relations by ranking with convolutional neural networks"
    - "traversing knowledge graphs in vector space"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-3 (induced by source paper "resolving event coreference with supervised representation learning and clustering oriented regularization"):
  with members:
    - "identity and granularity of events in text"
    - "event coreference resolution by iteratively unfolding inter dependencies among events"
    - "improving coreference resolution by learning entity level distributed representations"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "event coreference resolution using neural network classifiers"):
  with members:
    - "event coreference resolution by iteratively unfolding inter dependencies among events"
    - "efficient estimation of word representations in vector space"

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"
    - "api design for machine learning software experiences from the scikit learn project"
    - "combination strategies for semantic role labeling"

Connected through hyperedge HE-6 (induced by source paper "identifying civilians killed by police with distantly supervised entity event extraction"):
  with members:
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "adadelta an adaptive learning rate method"
    - "feature hashing for large scale multitask learning"

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.CL(Computation and Language)