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: "inferring interpersonal relations in narrative summaries"
  Abstract: Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "tifi taxonomy induction for fictional domains extended version"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "taxonomy induction using hypernym subsequences"
    - "augur mining human behaviors from fiction to power interactive systems"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "you re mr lebowski i m the dude inducing address term formality in signed social networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "no permanent friends or enemies tracking relationships between nations from news"):
  with members:
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "you are no jack kennedy on media selection of highlights from presidential debates"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "rookie a unique approach for exploring news archives"
    - "quotus the structure of political media coverage as revealed by quoting patterns"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "learning personas from dialogue with attentive memory networks"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a persona based neural conversation model"
    - "key value memory networks for directly reading documents"
    - "end to end memory networks"
    - "skip thought vectors"
    - "neural responding machine for short text conversation"
    - "a neural conversational model"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "predicting the quality of short narratives from social media"):
  with members:
    - "a theme rewriting approach for generating algebra word problems"
    - "automatic text scoring using neural networks"
    - "can cascades be predicted"
    - "distributed representations of words and phrases and their compositionality"

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)