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: "practical approach to knowledge based question answering with natural language understanding and advanced reasoning"
  Abstract: The complexity of natural language and the open-domain nature of the World Wide Web have caused modem-day question answering systems to rely only on information retrieval techniques and shallow natural language processing tasks. This approach has brought about serious drawbacks namely restriction on the nature of question and response. This restriction constitutes the first problem addressed by this#R##N#research. Through recent academic works, many researchers have begun to acknowledge the problem and agreed that the solution comes in the form of a new approach based on natural language understanding and reasoning in a knowledge-based environment. Due to the infancy stage of this new approach and practical consideration, the current practices vary greatly and are mostly based on only low-level natural language understanding, minimalist representation formalism and conventional reasoning approach without advanced features. As a result, not only were these systems found to be inadequate to solve the first problem but have also created the second problem, that is the limitation to scale across domains and to real-life natural language text. This research hypothesized that a practical approach in the form of a solution framework which combines full-discourse natural language understanding, powerful representation formalism capable of exploiting ontological information and reasoning approach with advanced features, will solve both the first and second problem without compromising practicality factors.The solution framework is implemented as a system called "Natural Language Understanding and#R##N#Reasoning for Intelligence" (NaLURI). More importantly, two evaluations and their results are presented to demonstrate that the inclusion of more demanding features into a question answering system will not only allow for a wider range of questions and better response quality, but does not affect the response time, hence approving the hypothesis of this research.

Connected through hyperedge HE-1 (induced by source paper "a survey on why type question answering systems"):
  with members:
    - "natural language processing almost from scratch"
    - "a survey of paraphrasing and textual entailment methods"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive comparison of machine learning based methods used in bengali question classification"):
  with members:
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-3 (induced by source paper "determining the unithood of word sequences using a probabilistic approach"):
  with members:
    - "determining the unithood of word sequences using mutual information and independence measure"

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)