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: "summarization from medical documents a survey"
  Abstract: Objective:: The aim of this paper is to survey the recent work in medical documents summarization. Background:: During the last decade, documents summarization got increasing attention by the AI research community. More recently it also attracted the interest of the medical research community as well, due to the enormous growth of information that is available to the physicians and researchers in medicine, through the large and growing number of published journals, conference proceedings, medical sites and portals on the World Wide Web, electronic medical records, etc. Methodology:: This survey gives first a general background on documents summarization, presenting the factors that summarization depends upon, discussing evaluation issues and describing briefly the various types of summarization techniques. It then examines the characteristics of the medical domain through the different types of medical documents. Finally, it presents and discusses the summarization techniques used so far in the medical domain, referring to the corresponding systems and their characteristics. Discussion and conclusions:: The paper discusses thoroughly the promising paths for future research in medical documents summarization. It mainly focuses on the issue of scaling to large collections of documents in various languages and from different media, on personalization issues, on portability to new sub-domains, and on the integration of summarization technology in practical applications.

Connected through hyperedge HE-1 (induced by source paper "text summarization in the biomedical domain"):
  with members:
    - "quantifying the informativeness for biomedical literature summarization an itemset mining method"
    - "different approaches for identifying important concepts in probabilistic biomedical text summarization"

Connected through hyperedge HE-2 (induced by source paper "quantifying the informativeness for biomedical literature summarization an itemset mining method"):
  with members:
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "nltk the natural language toolkit"

Connected through hyperedge HE-3 (induced by source paper "automated text summarisation and evidence based medicine a survey of two domains"):
  with members:
    - "ranking suspected answers to natural language questions using predictive annotation"
    - "centroid based summarization of multiple documents sentence extraction utility based evaluation and user studies"

Connected through hyperedge HE-4 (induced by source paper "different approaches for identifying important concepts in probabilistic biomedical text summarization"):
  with members:
    - "quantifying the informativeness for biomedical literature summarization an itemset mining method"
    - "bayesian query focused summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-5 (induced by source paper "a bayesian approach to biomedical text summarization"):
  with members:
    - "bayesian query focused summarization"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-6 (induced by source paper "using synchronic and diachronic relations for summarizing multiple documents describing evolving events"):
  with members:
    - "an introduction to the summarization of evolving events linear and non linear evolution"
    - "summarizing reports on evolving events part i linear evolution"
    - "exploiting cross document relations for multi document evolving summarization"
    - "ellogon a new text engineering platform"

Connected through hyperedge HE-7 (induced by source paper "some reflections on the task of content determination in the context of multi document summarization of evolving events"):
  with members:
    - "using synchronic and diachronic relations for summarizing multiple documents describing evolving events"
    - "an introduction to the summarization of evolving events linear and non linear evolution"
    - "summarizing reports on evolving events part i linear evolution"
    - "exploiting cross document relations for multi document evolving summarization"

Connected through hyperedge HE-8 (induced by source paper "an introduction to the summarization of evolving events linear and non linear evolution"):
  with members:
    - "exploiting cross document relations for multi document evolving summarization"

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)