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: "reflections on sentiment opinion analysis"
  Abstract: The detection of expressions of sentiment in online text has become a popular Natural Language Processing application. The task is commonly defined as identifying the words or phrases in a given fragment of text in which the reader understands that the author expresses some person’s positive, negative, or perhaps neutral attitude toward a topic. These four elements—expression words, attitude holder, topic, and attitude value—have evolved with hardly any discussion in the literature about their foundation or nature. Specifically, the use of two (or three) attitude values is far more simplistic than many examples of real language show. In this paper we ask: where do sentiments come from? We focus on two basic sources of human attitude—the holder’s non-logical/emotional preferences and the fulfillment of the holder’s goals. After exploring each source we provide a notional algorithm sketch and examples of how sentiment systems could provide richer and more realistic accounts of sentiment in text.

Connected through hyperedge HE-1 (induced by source paper "ranking and selecting multi hop knowledge paths to better predict human needs"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "deep learning for sentiment analysis a survey"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "deep contextualized word representations"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "commonsense for generative multi hop question answering tasks"
    - "simulating action dynamics with neural process networks"

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)