human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "detecting reliable novel word senses a network centric approach"):
  Abstract: In this era of Big Data, due to expeditious exchange of information on the web, words are being used to denote newer meanings, causing linguistic shift. With the recent availability of large amounts of digitized texts, an automated analysis of the evolution of language has become possible. Our study mainly focuses on improving the detection of new word senses. This paper presents a unique proposal based on network features to improve the precision of new word sense detection. For a candidate word where a new sense (birth) has been detected by comparing the sense clusters induced at two different time points, we further compare the network properties of the subgraphs induced from novel sense cluster across these two time points. Using the mean fractional change in edge density, structural similarity and average path length as features in an SVM classifier, manual evaluation gives precision values of 0.86 and 0.74 for the task of new sense detection, when tested on 2 distinct time-point pairs, in comparison to the precision values in the range of 0.23-0.32, when the proposed scheme is not used. The outlined method can therefore be used as a new post-hoc step to improve the precision of novel word sense detection in a robust and reliable way where the underlying framework uses a graph structure. Another important observation is that even though our proposal is a post-hoc step, it can be used in isolation and that itself results in a very decent performance achieving a precision of 0.54-0.62. Finally, we show that our method is able to detect the well-known historical shifts in 80% cases.

Includes members:
  Member "network features based co hyponymy detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using distributional thesaurus embedding for co hyponymy detection")
  Member "dynamic word embeddings for evolving semantic discovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis")
  Member "on the linearity of semantic change investigating meaning variation via dynamic graph models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning dynamic author representations with temporal language models")
  Member "cultural shift or linguistic drift comparing two computational measures of semantic change":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simulating lexical semantic change from sense annotated data")
  Member "diachronic word embeddings reveal statistical laws of semantic change":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a framework for the computational linguistic analysis of dehumanization")
  Member "statistically significant detection of linguistic change":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a framework for the computational linguistic analysis of dehumanization")
  Member "that s sick dude automatic identification of word sense change across different timescales":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time out temporal referencing for robust modeling of lexical semantic change")

Question: Please tell me which class the source paper 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)