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: "automatic labeling for entity extraction in cyber security"
  Abstract: Timely analysis of cyber-security information necessitates automated information extraction from unstructured text. While state-of-the-art extraction methods produce extremely accurate results, they require ample training data, which is generally unavailable for specialized applications, such as detecting security related entities; moreover, manual annotation of corpora is very costly and often not a viable solution. In response, we develop a very precise method to automatically label text from several data sources by leveraging related, domainspecific, structured data and provide public access to a corpus annotated with cyber-security entities. Next, we implement a Maximum Entropy Model trained with the average perceptron on a portion of our corpus ( 750,000 words) and achieve near perfect precision, recall, and accuracy, with training times under 17 seconds.

Connected through hyperedge HE-1 (induced by source paper "detecting cybersecurity events from noisy short text"):
  with members:
    - "learning domain specific word embeddings from sparse cybersecurity texts"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "vqa visual question answering"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "relext relation extraction using deep learning approaches for cybersecurity knowledge graph improvement"):
  with members:
    - "thinking fast and slow combining vector spaces and knowledge graphs"
    - "relation extraction a survey"
    - "towards a relation extraction framework for cyber security concepts"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "mining actionable information from security forums the case of malicious ip addresses"):
  with members:
    - "trollspot detecting misbehavior in commenting platforms"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "the anatomy of the facebook social graph"

Connected through hyperedge HE-4 (induced by source paper "ripex extracting malicious ip addresses from security forums using cross forum learning"):
  with members:
    - "towards a relation extraction framework for cyber security concepts"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "towards a relation extraction framework for cyber security concepts"):
  with members:
    - "pace pattern accurate computationally efficient bootstrapping for timely discovery of cyber security concepts"

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)