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: "boundary based mwe segmentation with text partitioning"
  Abstract: In this article we present a novel algorithm for the task of comprehensively segmenting texts into MWEs. With the basis for this algorithm (referred to as text partitioning) being recently developed, these results constitute its first performance-evaluated application to a natural language processing task. A differentiating feature of this single-parameter model is its focus on gap (i.e., punctuation) crossings as features for MWE identification, which uses substantially more information in training than is present in dictionaries. We show that this algorithm is capable of achieving high levels of precision and recall, using only type-level information, and then extend it to include part-of-speech tags to increase its performance to state-of-the-art levels, despite a simple decision criterion and general feature space (which makes the method directly applicable to other languages). Since the existence of comprehensive MWE annotations are what drive this segmentation algorithm, these results support their continued production. In addition, we have updated and extended the strength-averaging evaluation scheme, allowing for a more accurate and fine-grained understanding of model performance, and leading us to affirm the differences in nature and identifiability between weakly- and strongly-linked MWEs, quantitatively.

Connected through hyperedge HE-1 (induced by source paper "towards a science of human stories using sentiment analysis and emotional arcs to understand the building blocks of complex social systems"):
  with members:
    - "ultradense word embeddings by orthogonal transformation"
    - "benchmarking sentiment analysis methods for large scale texts a case for using continuum scored words and word shift graphs"
    - "probabilistic frame induction"
    - "learning frames from text with an unsupervised latent variable model"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "twitter mood predicts the stock market"

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)