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: "cfo conditional focused neural question answering with large scale knowledge bases"
  Abstract: How can we enable computers to automatically answer questions like "Who created the character Harry Potter"? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions --- ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neural-network-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions - the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%.

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"
    - "simple question answering by attentive convolutional neural network"
    - "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 "hhh an online medical chatbot system based on knowledge graph and hierarchical bi directional attention"):
  with members:
    - "finding answers from the word of god domain adaptation for neural networks in biblical question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improved neural relation detection for knowledge base question answering"
    - "attention is all you need"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "a question entailment approach to question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "cmu livemedqa at trec 2017 liveqa a consumer health question answering system"
    - "neural question answering at bioasq 5b"

Connected through hyperedge HE-8 (induced by source paper "one shot learning for question answering in gaokao history challenge"):
  with members:
    - "a question focused multi factor attention network for question answering"
    - "subword augmented embedding for cloze reading comprehension"
    - "sjtu nlp at semeval 2018 task 9 neural hypernym discovery with term embeddings"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "semi supervised qa with generative domain adaptive nets"
    - "bilateral multi perspective matching for natural language sentences"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"

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)