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 "a general fofe net framework for simple and effective question answering over knowledge bases"):
  Abstract: Question answering over knowledge base (KB-QA) has recently become a popular research topic in NLP. One popular way to solve the KB-QA problem is to make use of a pipeline of several NLP modules, including entity discovery and linking (EDL) and relation detection. Recent success on KB-QA task usually involves complex network structures with sophisticated heuristics. Inspired by a previous work that builds a strong KB-QA baseline, we propose a simple but general neural model composed of fixed-size ordinally forgetting encoding (FOFE) and deep neural networks, called FOFE-net to solve KB-QA problem at different stages. For evaluation, we use two popular KB-QA datasets, SimpleQuestions and WebQSP, and a newly created dataset, FreebaseQA. The experimental results show that FOFE-net performs well on KB-QA subtasks, entity discovery and linking (EDL) and relation detection, and in turn pushing overall KB-QA system to achieve strong results on all datasets.

Includes members:
  Member "question answering over freebase via attentive rnn with similarity matrix based cnn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "short text classification via knowledge powered attention with similarity matrix based cnn")
  Member "triviaqa a large scale distantly supervised challenge dataset for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "strong baselines for simple question answering over knowledge graphs with and without neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "neural networks models for entity discovery and linking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi task learning approach for named entity recognition using local detection")
  Member "s mart novel tree based structured learning algorithms applied to tweet entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs")
  Member "simple question answering by attentive convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "character level question answering with attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "large scale simple question answering with memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")

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)