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: "strong baselines for simple question answering over knowledge graphs with and without neural networks"
  Abstract: We examine the problem of question answering over knowledge graphs, focusing on simple questions that can be answered by the lookup of a single fact. Adopting a straightforward decomposition of the problem into entity detection, entity linking, relation prediction, and evidence combination, we explore simple yet strong baselines. On the SimpleQuestions dataset, we find that baseline LSTMs and GRUs plus a few heuristics yield accuracies that approach the state of the art, and techniques that do not use neural networks also perform reasonably well. These results show that gains from sophisticated deep learning techniques proposed in the literature are quite modest and that some previous models exhibit unnecessary complexity.

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"
    - "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"
    - "cfo conditional focused neural question answering with large scale knowledge bases"

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"
    - "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"
    - "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 "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 "cognitive knowledge graph reasoning for one shot relational learning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "meta learning for low resource neural machine translation"
    - "relational inductive biases deep learning and graph networks"
    - "variational knowledge graph reasoning"
    - "one shot relational learning for knowledge graphs"

Connected through hyperedge HE-6 (induced by source paper "simple attention based representation learning for ranking short social media posts"):
  with members:
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "deep relevance ranking using enhanced document query interactions"
    - "simplequestions nearly solved a new upperbound and baseline approach"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "making neural qa as simple as possible but not simpler"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "two birds one stone a simple unified model for text generation from structured and unstructured data"):
  with members:
    - "learning to generate questions by learningwhat not to generate"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "improving neural question generation using answer separation"
    - "sequential copying networks"
    - "learning to ask neural question generation for reading comprehension"
    - "on the state of the art of evaluation in neural language models"
    - "neural question generation from text a preliminary study"
    - "question answering and question generation as dual tasks"

Connected through hyperedge HE-8 (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"
    - "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"

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)