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: "grasping the finer point a supervised similarity network for metaphor detection"
  Abstract: The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on hand-engineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

Connected through hyperedge HE-1 (induced by source paper "learning outside the box discourse level features improve metaphor identification"):
  with members:
    - "deep contextualized word representations"
    - "neural metaphor detection in context"
    - "xgboost a scalable tree boosting system"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "skip thought vectors"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"):
  with members:
    - "are you serious rhetorical questions and sarcasm in social media dialog"
    - "a large self annotated corpus for sarcasm"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "automatic sarcasm detection a survey"

Connected through hyperedge HE-3 (induced by source paper "neural metaphor detection in context"):
  with members:
    - "deep contextualized word representations"
    - "adam a method for stochastic optimization"

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)