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 "simple question answering with subgraph ranking and joint scoring"):
  Abstract: Knowledge graph based simple question answering (KBSQA) is a major area of research within question answering. Although only dealing with simple questions, i.e., questions that can be answered through a single knowledge base (KB) fact, this task is neither simple nor close to being solved. Targeting on the two main steps, subgraph selection and fact selection, the research community has developed sophisticated approaches. However, the importance of subgraph ranking and leveraging the subject--relation dependency of a KB fact have not been sufficiently explored. Motivated by this, we present a unified framework to describe and analyze existing approaches. Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores. Our methods achieve a new state of the art (85.44% in accuracy) on the SimpleQuestions dataset.

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 "simplequestions nearly solved a new upperbound and baseline approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pretrained transformers for simple question answering over knowledge graphs")
  Member "improved neural relation detection for knowledge base question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "variational reasoning for question answering with knowledge graph":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "strong baselines for simple question answering over knowledge graphs with and without neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural relation prediction for simple question answering over knowledge graph")
  Member "question answering via integer programming over semi structured knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "simple question answering by attentive convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")

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)