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: "bringing structure into summaries crowdsourcing a benchmark corpus of concept maps"
  Abstract: Concept maps can be used to concisely represent important information and bring structure into large document collections. Therefore, we study a variant of multi-document summarization that produces summaries in the form of concept maps. However, suitable evaluation datasets for this task are currently missing. To close this gap, we present a newly created corpus of concept maps that summarize heterogeneous collections of web documents on educational topics. It was created using a novel crowdsourcing approach that allows us to efficiently determine important elements in large document collections. We release the corpus along with a baseline system and proposed evaluation protocol to enable further research on this variant of summarization.

Connected through hyperedge HE-1 (induced by source paper "query specific knowledge summarization with entity evolutionary networks"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "network inference via the time varying graphical lasso"

Connected through hyperedge HE-2 (induced by source paper "topexnet entity centric network topic exploration in news streams"):
  with members:
    - "word network topic model a simple but general solution for short and imbalanced texts"

Connected through hyperedge HE-3 (induced by source paper "soft layer specific multi task summarization with entailment and question generation"):
  with members:
    - "toward abstractive summarization using semantic representations"
    - "learning to ask neural question generation for reading comprehension"
    - "text summarization using abstract meaning representation"
    - "get to the point summarization with pointer generator networks"
    - "one model to learn them all"
    - "what do neural machine translation models learn about morphology"
    - "a deep reinforced model for abstractive summarization"
    - "cross stitch networks for multi task 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)