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: "hybrid memnet for extractive summarization"
  Abstract: Extractive text summarization has been an extensive research problem in the field of natural language understanding. While the conventional approaches rely mostly on manually compiled features to generate the summary, few attempts have been made in developing data-driven systems for extractive summarization. To this end, we present a fully data-driven end-to-end deep network which we call as Hybrid MemNet for single document summarization task. The network learns the continuous unified representation of a document before generating its summary. It jointly captures local and global sentential information along with the notion of summary worthy sentences. Experimental results on two different corpora confirm that our model shows significant performance gains compared with the state-of-the-art baselines.

Connected through hyperedge HE-1 (induced by source paper "unity in diversity learning distributed heterogeneous sentence representation for extractive summarization"):
  with members:
    - "neural summarization by extracting sentences and words"
    - "neural tree indexers for text understanding"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree structured composition in neural networks without tree structured architectures"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "abstractive summarization of reddit posts with multi level memory networks"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "generating wikipedia by summarizing long sequences"
    - "bottom up abstractive summarization"
    - "ranking sentences for extractive summarization with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "iterative document representation learning towards summarization with polishing"):
  with members:
    - "learning to extract coherent summary via deep reinforcement learning"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "sengen sentence generating neural variational topic model"
    - "get to the point summarization with pointer generator networks"
    - "neural summarization by extracting sentences and words"
    - "dynamic memory networks for visual and textual question answering"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "tensorflow a system for large scale machine learning"

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)