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: "end to end learning for short text expansion"
  Abstract: Effectively making sense of short texts is a critical task for many real world applications such as search engines, social media services, and recommender systems. The task is particularly challenging as a short text contains very sparse information, often too sparse for a machine learning algorithm to pick up useful signals. A common practice for analyzing short text is to first expand it with external information, which is usually harvested from a large collection of longer texts. In literature, short text expansion has been done with all kinds of heuristics. We propose an end-to-end solution that automatically learns how to expand short text to optimize a given learning task. A novel deep memory network is proposed to automatically find relevant information from a collection of longer documents and reformulate the short text through a gating mechanism. Using short text classification as a demonstrating task, we show that the deep memory network significantly outperforms classical text expansion methods with comprehensive experiments on real world data sets.

Connected through hyperedge HE-1 (induced by source paper "short text classification via term graph"):
  with members:
    - "joint embedding of words and labels for text classification"
    - "bag of tricks for efficient text classification"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values"):
  with members:
    - "megan a generative adversarial network for multi view network embedding"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "joint modeling of dense and incomplete trajectories for citywide traffic volume inference"
    - "brits bidirectional recurrent imputation for time series"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "a memory network based solution for multivariate time series forecasting"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "neural document expansion with user feedback"):
  with members:
    - "end to end neural ad hoc ranking with kernel pooling"
    - "attention is all you need"
    - "neural ranking models with multiple document fields"
    - "a deep relevance matching model for ad hoc retrieval"

Connected through hyperedge HE-4 (induced by source paper "images don t lie duplicate crowdtesting reports detection with screenshot information"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "distributed representations of words and phrases and their compositionality"

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)