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 "learning frames from text with an unsupervised latent variable model"):
  Abstract: We develop a probabilistic latent-variable model to discover semantic frames---types of events and their participants---from corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the model learns, and compare it to FrameNet, noting it learns some novel and interesting frames. This document also contains a discussion of inference issues, including concentration parameter learning; and a small-scale error analysis of syntactic parsing accuracy.

Includes members:
  Member "probabilistic frame induction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "watset local global graph clustering with applications in sense and frame induction")
  Member "topic models conditioned on arbitrary features with dirichlet multinomial regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "homogeneity based transmissive process to model true and false news in social networks")
  Member "a reliable effective terascale linear learning system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stannis low power acceleration of deep neural network training using computational storage")
  Member "syntactic topic models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic evaluation of local topic quality")
  Member "inducing a semantically annotated lexicon via em based clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiplex word embeddings for selectional preference acquisition")

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)