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 "semi supervised and unsupervised methods for categorizing posts in web discussion forums"):
  Abstract: Web discussion forums are used by millions of people worldwide to share information belonging to a variety of domains such as automotive vehicles, pets, sports, etc. They typically contain posts that fall into different categories such as problem, solution, feedback, spam, etc. Automatic identification of these categories can aid information retrieval that is tailored for specific user requirements. Previously, a number of supervised methods have attempted to solve this problem; however, these depend on the availability of abundant training data. A few existing unsupervised and semi-supervised approaches are either focused on identifying a single category or do not report category-specific performance. In contrast, this work proposes unsupervised and semi-supervised methods that require no or minimal training data to achieve this objective without compromising on performance. A fine-grained analysis is also carried out to discuss their limitations. The proposed methods are based on sequence models (specifically, Hidden Markov Models) that can model language for each category using word and part-of-speech probability distributions, and manually specified features. Empirical evaluations across domains demonstrate that the proposed methods are better suited for this task than existing ones.

Includes members:
  Member "distributed representations of sentences and documents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "catching the drift probabilistic content models with applications to generation and summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic natural language generation using dependency information")

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)