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: "mining and modeling character networks"
  Abstract: We investigate social networks of characters found in cultural works such as novels and films. These character networks exhibit many of the properties of complex networks such as skewed degree distribution and community structure, but may be of relatively small order with a high multiplicity of edges. Building on recent work of beveridge, we consider graph extraction, visualization, and network statistics for three novels: Twilight by Stephanie Meyer, Steven King's The Stand, and J.K. Rowling's Harry Potter and the Goblet of Fire. Coupling with 800 character networks from films found in the this http URL database, we compare the data sets to simulations from various stochastic complex networks models including random graphs with given expected degrees (also known as the Chung-Lu model), the configuration model, and the preferential attachment model. Using machine learning techniques based on motif (or small subgraph) counts, we determine that the Chung-Lu model best fits character networks and we conjecture why this may be the case.

Connected through hyperedge HE-1 (induced by source paper "temporal embeddings and transformer models for narrative text understanding"):
  with members:
    - "extraction and analysis of fictional character networks a survey"
    - "contextualized word embeddings enhanced event temporal relation extraction for story understanding"
    - "training temporal word embeddings with a compass"
    - "story ending prediction by transferable bert"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "diachronic word embeddings and semantic shifts a survey"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "extraction and analysis of fictional character networks a survey"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "conversational networks for automatic online moderation"
    - "remembering winter was coming"
    - "predicting kills in game of thrones using network properties"
    - "multilayer network model of movie script"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "i will be there for you six friends in a clique"
    - "the one comparing narrative social network extraction techniques"

Connected through hyperedge HE-3 (induced by source paper "creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain"):
  with members:
    - "redefining context windows for word embedding models an experimental study"
    - "high risk learning acquiring new word vectors from tiny data"
    - "the effects of data size and frequency range on distributional semantic models"
    - "enriching word vectors with subword information"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "issues in evaluating semantic spaces using word analogies"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-4 (induced by source paper "relation extraction datasets in the digital humanities domain and their evaluation with word embeddings"):
  with members:
    - "negative sampling improves hypernymy extraction based on projection learning"
    - "enriching word vectors with subword information"
    - "enhancing the lexvec distributed word representation model using positional contexts and external memory"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "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.SI(Social and Information Networks)