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: "no need to pay attention simple recurrent neural networks work for answering simple questions"
  Abstract: First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65%-76% accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results --- even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast's X1 entertainment platform with millions of users every day.

Connected through hyperedge HE-1 (induced by source paper "using context information to enhance simple question answering"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "freebase triples a methodology for processing the freebase data dumps"
    - "attention is all you need"
    - "semi supervised multitask learning for sequence labeling"
    - "kbqa learning question answering over qa corpora and knowledge bases"

Connected through hyperedge HE-2 (induced by source paper "the combination of context information to enhance simple question answering"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "freebase triples a methodology for processing the freebase data dumps"
    - "cfo conditional focused neural question answering with large scale knowledge bases"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"

Connected through hyperedge HE-3 (induced by source paper "learning structured natural language representations for semantic parsing"):
  with members:
    - "recurrent neural network grammars"
    - "evaluating induced ccg parsers on grounded semantic parsing"
    - "learning a natural language interface with neural programmer"
    - "rationalizing neural predictions"
    - "data recombination for neural semantic parsing"
    - "semantic parsing with semi supervised sequential autoencoders"
    - "question answering on freebase via relation extraction and textual evidence"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-4 (induced by source paper "strong baselines for simple question answering over knowledge graphs with and without neural networks"):
  with members:
    - "improved neural relation detection for knowledge base question answering"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "simple question answering by attentive convolutional neural network"
    - "large scale simple question answering with memory networks"

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)