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: "modeling attractiveness and multiple clicks in sponsored search results"
  Abstract: Click models are an important tool for leveraging user feedback, and are used by commercial search engines for surfacing relevant search results. However, existing click models are lacking in two aspects. First, they do not share information across search results when computing attractiveness. Second, they assume that users interact with the search results sequentially. Based on our analysis of the click logs of a commercial search engine, we observe that the sequential scan assumption does not always hold, especially for sponsored search results. To overcome the above two limitations, we propose a new click model. Our key insight is that sharing information across search results helps in identifying important words or key-phrases which can then be used to accurately compute attractiveness of a search result. Furthermore, we argue that the click probability of a position as well as its attractiveness changes during a user session and depends on the user's past click experience. Our model seamlessly incorporates the effect of externalities (quality of other search results displayed in response to a user query), user fatigue, as well as pre and post-click relevance of a sponsored search result. We propose an efficient one-pass inference scheme and empirically evaluate the performance of our model via extensive experiments using the click logs of a large commercial search engine.

Connected through hyperedge HE-1 (induced by source paper "personalized attraction enhanced sponsored search with multi task learning"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "learning multiple tasks with deep relationship networks"
    - "truthful learning mechanisms for multi slot sponsored search auctions with externalities"
    - "distributed representations of words and phrases and their compositionality"
    - "budget optimization in search based advertising auctions"

Connected through hyperedge HE-2 (induced by source paper "can boosting with svm as week learners help"):
  with members:
    - "controlled sparsity kernel learning"

Connected through hyperedge HE-3 (induced by source paper "finding optimal combination of kernels using genetic programming"):
  with members:
    - "controlled sparsity kernel learning"

Connected through hyperedge HE-4 (induced by source paper "comparing face detection and recognition techniques"):
  with members:
    - "controlled sparsity kernel learning"

Connected through hyperedge HE-5 (induced by source paper "accessing accurate documents by mining auxiliary document information"):
  with members:
    - "controlled sparsity kernel learning"

Connected through hyperedge HE-6 (induced by source paper "colors messengers of concepts visual design mining for learning color semantics"):
  with members:
    - "indoor semantic segmentation using depth information"

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)