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: "improved neural relation detection for knowledge base question answering"
  Abstract: Relation detection is a core component for many NLP applications including Knowledge Base Question Answering (KBQA). In this paper, we propose a hierarchical recurrent neural network enhanced by residual learning that detects KB relations given an input question. Our method uses deep residual bidirectional LSTMs to compare questions and relation names via different hierarchies of abstraction. Additionally, we propose a simple KBQA system that integrates entity linking and our proposed relation detector to enable one enhance another. Experimental results evidence that our approach achieves not only outstanding relation detection performance, but more importantly, it helps our KBQA system to achieve state-of-the-art accuracy for both single-relation (SimpleQuestions) and multi-relation (WebQSP) QA benchmarks.

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"
    - "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"
    - "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"
    - "attention is all you need"

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"
    - "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"
    - "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"
    - "attention is all you need"
    - "kbqa learning question answering over qa corpora and knowledge bases"
    - "cfo conditional focused neural question answering with large scale 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 "jec qa a legal domain question answering dataset"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "interpretable charge predictions for criminal cases learning to generate court views from fact descriptions"
    - "cail2018 a large scale legal dataset for judgment prediction"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "know what you don t know unanswerable questions for squad"
    - "think you have solved question answering try arc the ai2 reasoning challenge"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "relation adversarial network for low resource knowledgegraph completion"):
  with members:
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "multi task feature learning for knowledge graph enhanced recommendation"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "kg bert bert for knowledge graph completion"
    - "ernie enhanced language representation with informative entities"
    - "iteratively learning embeddings and rules for knowledge graph reasoning"
    - "transfer learning for relation extraction via relation gated adversarial learning"
    - "meta relational learning for few shot link prediction in knowledge graphs"

Connected through hyperedge HE-8 (induced by source paper "attention guided graph convolutional networks for relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "n ary relation extraction using graph state lstm"
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"

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)