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: "enhancing qa systems with complex temporal question processing capabilities"
  Abstract: This paper presents a multilayered architecture that enhances the capabilities of current QA systems and allows different types of complex questions or queries to be processed. The answers to these questions need to be gathered from factual information scattered throughout different documents. Specifically, we designed a specialized layer to process the different types of temporal questions. Complex temporal questions are first decomposed into simple questions, according to the temporal relations expressed in the original question. In the same way, the answers to the resulting simple questions are recomposed, fulfilling the temporal restrictions of the original complex question. A novel aspect of this approach resides in the decomposition which uses a minimal quantity of resources, with the final aim of obtaining a portable platform that is easily extensible to other languages. In this paper we also present a methodology for evaluation of the decomposition of the questions as well as the ability of the implemented temporal layer to perform at a multilingual level. The temporal layer was first performed for English, then evaluated and compared with: a) a general purpose QA system (F-measure 65.47% for QA plus English temporal layer vs. 38.01% for the general QA system), and b) a well-known QA system. Much better results were obtained for temporal questions with the multilayered system. This system was therefore extended to Spanish and very good results were again obtained in the evaluation (F-measure 40.36% for QA plus Spanish temporal layer vs. 22.94% for the general QA system).

Connected through hyperedge HE-1 (induced by source paper "comqa a community sourced dataset for complex factoid question answering with paraphrase clusters"):
  with members:
    - "the web as a knowledge base for answering complex questions"
    - "know what you don t know unanswerable questions for squad"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "race large scale reading comprehension dataset from examinations"
    - "learning to paraphrase for question answering"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "the narrativeqa reading comprehension challenge"
    - "newsqa a machine comprehension dataset"

Connected through hyperedge HE-2 (induced by source paper "tequila temporal question answering over knowledge bases"):
  with members:
    - "question answering on freebase via relation extraction and textual evidence"
    - "towards universal paraphrastic sentence embeddings"
    - "learning dependency based compositional semantics"

Connected through hyperedge HE-3 (induced by source paper "timeml strict clarifying temporal annotation"):
  with members:
    - "analysing temporally annotated corpora with cavat"
    - "tempeval 3 evaluating events time expressions and temporal relations"
    - "massively increasing timex3 resources a transduction approach"

Connected through hyperedge HE-4 (induced by source paper "a corpus based study of temporal signals"):
  with members:
    - "analysing temporally annotated corpora with cavat"
    - "using signals to improve automatic classification of temporal relations"
    - "learning sentence internal temporal relations"

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)