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 on freebase via relation extraction and textual evidence"
  Abstract: Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F_1 of 53.3%, a substantial improvement over the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "realistic re evaluation of knowledge graph completion methods an experimental study"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "on evaluating embedding models for knowledge base completion"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "convolutional 2d knowledge graph embeddings"
    - "proje embedding projection for knowledge graph completion"

Connected through hyperedge HE-2 (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"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "large scale simple question answering with memory networks"

Connected through hyperedge HE-3 (induced by source paper "coordinated reasoning for cross lingual knowledge graph alignment"):
  with members:
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "cross lingual entity alignment via joint attribute preserving embedding"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "multilingual knowledge graph embeddings for cross lingual knowledge alignment"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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-5 (induced by source paper "discourse aware semantic self attention for narrative reading comprehension"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "transformer xl attentive language models beyond a fixed length context"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "self attention with relative position representations"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "mcscript a novel dataset for assessing machine comprehension using script knowledge"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-6 (induced by source paper "bert post training for review reading comprehension and aspect based sentiment analysis"):
  with members:
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "double embeddings and cnn based sequence labeling for aspect extraction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"

Connected through hyperedge HE-7 (induced by source paper "review conversational reading comprehension"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "drcd a chinese machine reading comprehension dataset"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"

Connected through hyperedge HE-8 (induced by source paper "multi task learning for conversational question answering over a large scale knowledge base"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "coarse to fine decoding for neural semantic parsing"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"
    - "a syntactic neural model for general purpose code generation"

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)