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: "language use matters analysis of the linguistic structure of question texts can characterize answerability in quora"
  Abstract: Quora is one of the most popular community Q&A sites of recent times. However, many question posts on this Q&A site often do not get answered. In this paper, we quantify various linguistic activities that discriminates an answered question from an unanswered one. Our central finding is that the way users use language while writing the question text can be a very effective means to characterize answerability. This characterization helps us to predict early if a question remaining unanswered for a specific time period t will eventually be answered or not and achieve an accuracy of 76.26% (t = 1 month) and 68.33% (t = 3 months). Notably, features representing the language use patterns of the users are most discriminative and alone account for an accuracy of 74.18%. We also compare our method with some of the similar works (Dror et al., Yang et al.) achieving a maximum improvement of ~39% in terms of accuracy.

Connected through hyperedge HE-1 (induced by source paper "competing topic naming conventions in quora predicting appropriate topic merges and winning topics from millions of topic pairs"):
  with members:
    - "universal sentence encoder"
    - "deep dive into anonymity a large scale analysis of quora questions"
    - "competition and selection among conventions"
    - "bieber blast bieberblast early prediction of popular hashtag compounds"
    - "analysis of named entity recognition and linking for tweets"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "who is authoritative understanding reputation mechanisms in quora"

Connected through hyperedge HE-2 (induced by source paper "learning to fingerprint the latent structure in question articulation"):
  with members:
    - "squad 100 000 questions for machine comprehension of text"
    - "adam a method for stochastic optimization"
    - "entropy based nearest neighbor search in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "deep dive into anonymity a large scale analysis of quora questions"):
  with members:
    - "empath understanding topic signals in large scale text"

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)