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 knowledge base with neural attention combining global knowledge information"
  Abstract: With the rapid growth of knowledge bases (KBs) on the web, how to take full advantage of them becomes increasingly important. Knowledge base-based question answering (KB-QA) is one of the most promising approaches to access the substantial knowledge. Meantime, as the neural network-based (NN-based) methods develop, NN-based KB-QA has already achieved impressive results. However, previous work did not put emphasis on question representation, and the question is converted into a fixed vector regardless of its candidate answers. This simple representation strategy is unable to express the proper information of the question. Hence, we present a neural attention-based model to represent the questions dynamically according to the different focuses of various candidate answer aspects. In addition, we leverage the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers. And it also alleviates the out of vocabulary (OOV) problem, which helps the attention model to represent the question more precisely. The experimental results on WEBQUESTIONS demonstrate the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "introduction to neural network based approaches for question answering over knowledge graphs"):
  with members:
    - "pretrained transformers for simple question answering over knowledge graphs"
    - "learning to generalize from sparse and underspecified rewards"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "practical semantic parsing for spoken language understanding"
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a question answering system over the semantic web"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"

Connected through hyperedge HE-2 (induced by source paper "learning attention based embeddings for relation prediction in knowledge graphs"):
  with members:
    - "multi hop knowledge graph reasoning with reward shaping"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "attention is all you need"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"
    - "modeling relation paths for representation learning of knowledge bases"

Connected through hyperedge HE-3 (induced by source paper "faq based question answering via knowledge anchors"):
  with members:
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "variational reasoning for question answering with knowledge graph"
    - "bidirectional attention flow for machine comprehension"
    - "dynamic memory networks for visual and textual question answering"
    - "bag of tricks for efficient text classification"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "text matching as image recognition"
    - "neural generative question answering"

Connected through hyperedge HE-4 (induced by source paper "out of the box reasoning with graph convolution nets for factual visual question answering"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "mutan multimodal tucker fusion for visual question answering"
    - "visual question generation as dual task of visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"
    - "high order attention models for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "finbrain when finance meets ai 2 0"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "struc2vec learning node representations from structural identity"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "neural collaborative filtering"
    - "end to end task completion neural dialogue systems"
    - "metacontrol for adaptive imagination based optimization"
    - "adversarial learning for neural dialogue generation"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-6 (induced by source paper "towards a question answering system over the semantic web"):
  with members:
    - "amuse multilingual semantic parsing for question answering over linked data"
    - "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"
    - "a graph traversal based approach to answer non aggregation questions over dbpedia"
    - "large scale simple question answering with memory networks"

Connected through hyperedge HE-7 (induced by source paper "a novel embedding model for knowledge base completion based on convolutional neural network"):
  with members:
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "search personalization with embeddings"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "convolutional 2d knowledge graph embeddings"
    - "neighborhood mixture model for knowledge base completion"
    - "link prediction using embedded knowledge graphs"
    - "stranse a novel embedding model of entities and relationships in knowledge bases"

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.IR(Information Retrieval)