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: "aff2vec affect enriched distributional word representations"
  Abstract: Human communication includes information, opinions, and reactions. Reactions are often captured by the affective-messages in written as well as verbal communications. While there has been work in affect modeling and to some extent affective content generation, the area of affective word distributions in not well studied. Synsets and lexica capture semantic relationships across words. These models however lack in encoding affective or emotional word interpretations. Our proposed model, Aff2Vec provides a method for enriched word embeddings that are representative of affective interpretations of words. Aff2Vec outperforms the state--of--the--art in intrinsic word-similarity tasks. Further, the use of Aff2Vec representations outperforms baseline embeddings in downstream natural language understanding tasks including sentiment analysis, personality detection, and frustration prediction.

Connected through hyperedge HE-1 (induced by source paper "learning word ratings for empathy and distress from document level user responses"):
  with members:
    - "joint emotion label space modelling for affect lexica"
    - "modeling empathy and distress in reaction to news stories"
    - "annotating and modeling empathy in spoken conversations"
    - "advances in pre training distributed word representations"
    - "a unified approach to interpreting model predictions"
    - "emotion intensities in tweets"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning neural emotion analysis from 100 observations the surprising effectiveness of pre trained word representations"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "learning word vectors for 157 languages"
    - "attention is all you need"
    - "wassa 2017 shared task on emotion intensity"
    - "advances in pre training distributed word representations"
    - "deep multitask learning for semantic dependency parsing"
    - "emotion intensities in tweets"
    - "enriching word vectors with subword information"

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)