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: "simple question answering by attentive convolutional neural network"
  Abstract: This work focuses on answering single-relation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA), can be addressed via a two-step pipeline: entity linking and fact selection. In fact selection, we match the subject entity in a fact candidate with the entity mention in the question by a character-level convolutional neural network (char-CNN), and match the predicate in that fact with the question by a word-level CNN (word-CNN). This work makes two main contributions. (i) A simple and effective entity linker over Freebase is proposed. Our entity linker outperforms the state-of-the-art entity linker over SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so that the predicate representation can be matched with the predicate-focused question representation more effectively. Experiments show that our system sets new state-of-the-art in this task.

Connected through hyperedge HE-1 (induced by source paper "neural relation prediction for simple question answering over knowledge graph"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "improved neural relation detection for knowledge base question answering"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "bilateral multi perspective matching for natural language sentences"
    - "attentive pooling networks"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-2 (induced by source paper "pretrained transformers for simple question answering over knowledge graphs"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "learning to rank query graphs for complex question answering over knowledge graphs"
    - "universal language model fine tuning for text classification"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-3 (induced by source paper "end to end entity linking and disambiguation leveraging word and knowledge graph embeddings"):
  with members:
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "improved neural relation detection for knowledge base question answering"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "convolutional 2d knowledge graph embeddings"
    - "cfo conditional focused neural question answering with large scale knowledge bases"
    - "character level question answering with attention"
    - "question answering on freebase via relation extraction and textual evidence"
    - "large scale simple question answering with memory networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "uhop an unrestricted hop relation extraction framework for knowledge based question answering"):
  with members:
    - "knowledge base relation detection via multi view matching"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "an attention based word level interaction model relation detection for knowledge base question answering"
    - "variational knowledge graph reasoning"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-6 (induced by source paper "towards scalable and reliable capsule networks for challenging nlp applications"):
  with members:
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "fast dynamic routing based on weighted kernel density estimation"
    - "zero shot user intent detection via capsule neural networks"
    - "towards linear time neural machine translation with capsule networks"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "dynamic routing between capsules"
    - "attentive pooling networks"

Connected through hyperedge HE-7 (induced by source paper "simple question answering with subgraph ranking and joint scoring"):
  with members:
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "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"
    - "character level question answering with attention"
    - "end to end memory networks"

Connected through hyperedge HE-8 (induced by source paper "learning representation mapping for relation detection in knowledge base question answering"):
  with members:
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "a teacher student framework for zero resource neural machine translation"
    - "improved neural relation detection for knowledge base question answering"
    - "zero shot relation extraction via reading comprehension"
    - "word translation without parallel data"
    - "zero resource translation with multi lingual neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"

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)