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 "aspect sentiment model for micro reviews"):
  Abstract: This paper aims at an aspect sentiment model for aspect-based sentiment analysis (ABSA) focused on micro reviews. This task is important in order to understand short reviews majority of the users write, while existing topic models are targeted for expert-level long reviews with sufficient co-occurrence patterns to observe. Current methods on aggregating micro reviews using metadata information may not be effective as well due to metadata absence, topical heterogeneity, and cold start problems. To this end, we propose a model called Micro Aspect Sentiment Model (MicroASM). MicroASM is based on the observation that short reviews 1) are viewed with sentiment-aspect word pairs as building blocks of information, and 2) can be clustered into larger reviews. When compared to the current state-of-the-art aspect sentiment models, experiments show that our model provides better performance on aspect-level tasks such as aspect term extraction and document-level tasks such as sentiment classification.

Includes members:
  Member "recursive neural conditional random fields for aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "inducing domain specific sentiment lexicons from unlabeled corpora":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating word and document embeddings for sentiment analysis")
  Member "a hierarchical model of reviews for aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis")
  Member "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a joint model of conversational discourse and latent topics on microblogs")
  Member "measuring praise and criticism inference of semantic orientation from association":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator")

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)