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: "question answering over freebase via attentive rnn with similarity matrix based cnn"
  Abstract: With the rapid growth of knowledge bases (KBs), question answering over knowledge base, a.k.a. KBQA has drawn huge attention in recent years. Most of the existing KBQA methods follow so called encoder-compare framework. They map the question and the KB facts to a common embedding space, in which the similarity between the question vector and the fact vectors can be conveniently computed. This, however, inevitably loses original words interaction information. To preserve more original information, we propose an attentive recurrent neural network with similarity matrix based convolutional neural network (AR-SMCNN) model, which is able to capture comprehensive hierarchical information utilizing the advantages of both RNN and CNN. We use RNN to capture semantic-level correlation by its sequential modeling nature, and use an attention mechanism to keep track of the entities and relations simultaneously. Meanwhile, we use a similarity matrix based CNN with two-directions pooling to extract literal-level words interaction matching utilizing CNNs strength of modeling spatial correlation among data. Moreover, we have developed a new heuristic extension method for entity detection, which significantly decreases the effect of noise. Our method has outperformed the state-of-the-arts on SimpleQuestion benchmark in both accuracy and efficiency.

Connected through hyperedge HE-1 (induced by source paper "short text classification via knowledge powered attention with similarity matrix based cnn"):
  with members:
    - "deep short text classification with knowledge powered attention"
    - "cn probase a data driven approach for large scale chinese taxonomy construction"
    - "clinical text classification with rule based features and knowledge guided convolutional neural networks"
    - "differentiating concepts and instances for knowledge graph embedding"
    - "multilingual hierarchical attention networks for document classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "a structured self attentive sentence embedding"
    - "very deep convolutional networks for natural language processing"

Connected through hyperedge HE-2 (induced by source paper "simple question answering with subgraph ranking and joint scoring"):
  with members:
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "improved neural relation detection for knowledge base question answering"
    - "variational reasoning for question answering with knowledge graph"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "question answering via integer programming over semi structured knowledge"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "end to end memory networks"

Connected through hyperedge HE-3 (induced by source paper "a general fofe net framework for simple and effective question answering over knowledge bases"):
  with members:
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "neural networks models for entity discovery and linking"
    - "s mart novel tree based structured learning algorithms applied to tweet entity linking"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "large scale simple question answering with memory networks"
    - "open question answering with weakly supervised embedding models"

Connected through hyperedge HE-4 (induced by source paper "learning to answer ambiguous questions with knowledge graph"):
  with members:
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "improved neural relation detection for knowledge base question answering"
    - "character level question answering with attention"
    - "enhancing freebase question answering using textual evidence"
    - "question answering on freebase via relation extraction and textual evidence"
    - "large scale simple question answering with memory networks"
    - "question answering with subgraph embeddings"

Connected through hyperedge HE-5 (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"
    - "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"
    - "no need to pay attention simple recurrent neural networks work for answering simple questions"

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)