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: "large margin learning of submodular summarization methods"
  Abstract: In this paper, we present a supervised learning approach to training submodular scoring functions for extractive multi-document summarization. By taking a structured predicition approach, we provide a large-margin method that directly optimizes a convex relaxation of the desired performance measure. The learning method applies to all submodular summarization methods, and we demonstrate its effectiveness for both pairwise as well as coverage-based scoring functions on multiple datasets. Compared to state-of-the-art functions that were tuned manually, our method significantly improves performance and enables high-fidelity models with numbers of parameters well beyond what could reasonbly be tuned by hand.

Connected through hyperedge HE-1 (induced by source paper "ins an interactive chinese news synthesis system"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "sequence to sequence rnns for text summarization"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-2 (induced by source paper "content selection in deep learning models of summarization"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "get to the point summarization with pointer generator networks"
    - "graph based neural multi document summarization"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "neural summarization by extracting sentences and words"
    - "learning based single document summarization with compression and anaphoricity constraints"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-3 (induced by source paper "differentiable greedy networks"):
  with members:
    - "attention is all you need"
    - "maximal sparsity with deep networks"
    - "enriching word vectors with subword information"
    - "rationalizing neural predictions"
    - "end to end learning of lda by mirror descent back propagation over a deep architecture"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-4 (induced by source paper "enumeration of extractive oracle summaries"):
  with members:
    - "multi document abstractive summarization using ilp based multi sentence compression"
    - "abstractive multi document summarization via phrase selection and merging"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-5 (induced by source paper "neural network based abstract generation for opinions and arguments"):
  with members:
    - "query focused opinion summarization for user generated content"
    - "a neural attention model for abstractive sentence summarization"
    - "abstractive multi document summarization via phrase selection and merging"
    - "teaching machines to read and comprehend"
    - "neural machine translation by jointly learning to align and translate"
    - "deep visual semantic alignments for generating image descriptions"
    - "sequence to sequence learning with neural networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "query focused opinion summarization for user generated content"):
  with members:
    - "integrating document clustering and topic modeling"
    - "bayesian query focused summarization"

Connected through hyperedge HE-7 (induced by source paper "abstractive multi document summarization via phrase selection and merging"):
  with members:
    - "reader aware multi document summarization via sparse coding"
    - "modelling visualising and summarising documents with a single convolutional neural network"
    - "learning mixtures of submodular shells with application to document summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-8 (induced by source paper "structured learning of sum of submodular higher order energy functions"):
  with members:
    - "learning mixtures of submodular shells with application to document summarization"
    - "minimizing a sum of submodular functions"

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.AI(Artificial Intelligence)