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: "dtatg an automatic title generator based on dependency trees"
  Abstract: We study automatic title generation for a given block of text and present a method called DTATG to generate titles. DTATG first extracts a small number of central sentences that convey the main meanings of the text and are in a suitable structure for conversion into a title. DTATG then constructs a dependency tree for each of these sentences and removes certain branches using a Dependency Tree Compression Model we devise. We also devise a title test to determine if a sentence can be used as a title. If a trimmed sentence passes the title test, then it becomes a title candidate. DTATG selects the title candidate with the highest ranking score as the final title. Our experiments showed that DTATG can generate adequate titles. We also showed that DTATG-generated titles have higher F1 scores than those generated by the previous methods.

Connected through hyperedge HE-1 (induced by source paper "generating an overview report over many documents"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "extract with order for coherent multi document summarization"
    - "automated phrase mining from massive text corpora"
    - "semi supervised classification with graph convolutional networks"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "a neural attention model for abstractive sentence summarization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-2 (induced by source paper "semi automatic system for title construction"):
  with members:
    - "scake semantic connectivity aware keyword extraction"

Connected through hyperedge HE-3 (induced by source paper "efficient and effective single document summarizations and a word embedding measurement of quality"):
  with members:
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "neural summarization by extracting sentences and words"
    - "a neural attention model for abstractive sentence summarization"
    - "efficient estimation of word representations in vector space"
    - "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.IR(Information Retrieval)