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: "s mart novel tree based structured learning algorithms applied to tweet entity linking"
  Abstract: Non-linear models recently receive a lot of attention as people are starting to discover the power of statistical and embedding features. However, tree-based models are seldom studied in the context of structured learning despite their recent success on various classification and ranking tasks. In this paper, we propose S-MART, a tree-based structured learning framework based on multiple additive regression trees. S-MART is especially suitable for handling tasks with dense features, and can be used to learn many different structures under various loss functions. #R##N#We apply S-MART to the task of tweet entity linking --- a core component of tweet information extraction, which aims to identify and link name mentions to entities in a knowledge base. A novel inference algorithm is proposed to handle the special structure of the task. The experimental results show that S-MART significantly outperforms state-of-the-art tweet entity linking systems.

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 "generating logical forms from graph representations of text and entities"):
  with members:
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "self attention with relative position representations"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "bidirectional attentive memory networks for question answering over knowledge bases"):
  with members:
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "universal semantic parsing"
    - "question answering on knowledge bases and text using universal schema and memory networks"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "bidirectional attention flow for machine comprehension"
    - "key value memory networks for directly reading documents"
    - "dynamic coattention networks for question answering"

Connected through hyperedge HE-4 (induced by source paper "uhop an unrestricted hop relation extraction framework for knowledge based question answering"):
  with members:
    - "knowledge base relation detection via multi view matching"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "an attention based word level interaction model relation detection for knowledge base question answering"
    - "variational knowledge graph reasoning"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-5 (induced by source paper "distantly supervised question parsing"):
  with members:
    - "introduction to neural network based approaches for question answering over knowledge graphs"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "building a conversational agent overnight with dialogue self play"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "practical bayesian optimization in the presence of outliers"
    - "learning to compose neural networks for question answering"
    - "learning to compose words into sentences with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a general fofe net framework for simple and effective question answering over knowledge bases"):
  with members:
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "strong baselines for simple question answering over knowledge graphs with and without neural networks"
    - "neural networks models for entity discovery and linking"
    - "simple question answering by attentive convolutional neural network"
    - "character level question answering with attention"
    - "large scale simple question answering with memory networks"
    - "open question answering with weakly supervised embedding models"

Connected through hyperedge HE-7 (induced by source paper "named entities troubling your neural methods build ne table a neural approach for handling named entities"):
  with members:
    - "deep contextualized word representations"
    - "improved neural relation detection for knowledge base question answering"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "pointing the unknown words"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "towards end to end reinforcement learning of dialogue agents for information access"

Connected through hyperedge HE-8 (induced by source paper "collective entity disambiguation with structured gradient tree boosting"):
  with members:
    - "deep joint entity disambiguation with local neural attention"
    - "neural cross lingual entity linking"
    - "globally normalized transition based neural networks"
    - "joint learning of the embedding of words and entities for named entity disambiguation"
    - "leveraging deep neural networks and knowledge graphs for entity disambiguation"
    - "probabilistic bag of hyperlinks model for entity linking"
    - "scikit learn machine learning in python"
    - "learning as search optimization approximate large margin methods for structured prediction"

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)