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: "automated prediction of temporal relations"
  Abstract: Background: There has been growing research interest in automated answering of questions or generation of summary of free form text such as news article. In order to implement this task, the computer should be able to identify the sequence of events, duration of events, time at which event occurred and the relationship type between event pairs, time pairs or event-time pairs. Specific Problem: It is important to accurately identify the relationship type between combinations of event and time before the temporal ordering of events can be defined. The machine learning approach taken in Mani et. al (2006) provides an accuracy of only 62.5 on the baseline data from TimeBank. The researchers used maximum entropy classifier in their methodology. TimeML uses the TLINK annotation to tag a relationship type between events and time. The time complexity is quadratic when it comes to tagging documents with TLINK using human annotation. This research proposes using decision tree and parsing to improve the relationship type tagging. This research attempts to solve the gaps in human annotation by automating the task of relationship type tagging in an attempt to improve the accuracy of event and time relationship in annotated documents. Scope information: The documents from the domain of news will be used. The tagging will be performed within the same document and not across documents. The relationship types will be identified only for a pair of event and time and not a chain of events. The research focuses on documents tagged using the TimeML specification which contains tags such as EVENT, TLINK, and TIMEX. Each tag has attributes such as identifier, relation, POS, time etc.

Connected through hyperedge HE-1 (induced by source paper "edge based grid super imposition for crowd emotion recognition"):
  with members:
    - "multimodal affect analysis for product feedback assessment"
    - "code definition analysis for call graph generation"
    - "structured unit testable templated code for efficient code review process"
    - "dynamic probabilistic network based human action recognition"
    - "self contained cross cutting pipeline software architecture"
    - "augmenting supervised emotion recognition with rule based decision model"
    - "multimodal affect recognition using kinect"
    - "xml entity architecture for efficient software integration"

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)