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: "search by ideal candidates next generation of talent search at linkedin"
  Abstract: One key challenge in talent search is how to translate complex criteria of a hiring position into a search query. This typically requires deep knowledge on which skills are typically needed for the position, what are their alternatives, which companies are likely to have such candidates, etc. However, listing examples of suitable candidates for a given position is a relatively easy job. Therefore, in order to help searchers overcome this challenge, we design a next generation of talent search paradigm at LinkedIn: Search by Ideal Candidates. This new system only needs the searcher to input one or several examples of suitable candidates for the position. The system will generate a query based on the input candidates and then retrieve and rank results based on the query as well as the input candidates. The query is also shown to the searcher to make the system transparent and to allow the searcher to interact with it. As the searcher modifies the initial query and makes it deviate from the ideal candidates, the search ranking function dynamically adjusts an refreshes the ranking results balancing between the roles of query and ideal candidates. As of writing this paper, the new system is being launched to our customers.

Connected through hyperedge HE-1 (induced by source paper "entity personalized talent search models with tree interaction features"):
  with members:
    - "talent search and recommendation systems at linkedin practical challenges and lessons learned"
    - "towards deep and representation learning for talent search at linkedin"
    - "from query by keyword to query by example linkedin talent search approach"
    - "xgboost a scalable tree boosting system"
    - "personalized expertise search at linkedin"

Connected through hyperedge HE-2 (induced by source paper "towards deep and representation learning for talent search at linkedin"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "wide deep learning for recommender systems"
    - "a deep relevance matching model for ad hoc retrieval"
    - "xgboost a scalable tree boosting system"
    - "personalized expertise search at linkedin"
    - "tensorflow a system for large scale machine learning"
    - "convolutional neural network architectures for matching natural language sentences"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "in session personalization for talent search"):
  with members:
    - "personalized expertise search at linkedin"
    - "incremental factorization machines for persistently cold starting online item recommendation"
    - "algorithm selection for combinatorial search problems a survey"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "bandit problems with side observations"

Connected through hyperedge HE-4 (induced by source paper "talent search and recommendation systems at linkedin practical challenges and lessons learned"):
  with members:
    - "in session personalization for talent search"
    - "towards deep and representation learning for talent search at linkedin"
    - "personalized expertise search at linkedin"
    - "learning to rank personalized search results in professional networks"

Connected through hyperedge HE-5 (induced by source paper "deploying deep ranking models for search verticals"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-6 (induced by source paper "talent flow analytics in online professional network"):
  with members:
    - "on analyzing job hop behavior and talent flow networks"

Connected through hyperedge HE-7 (induced by source paper "from query by keyword to query by example linkedin talent search approach"):
  with members:
    - "personalized expertise search at linkedin"

Connected through hyperedge HE-8 (induced by source paper "learning to rank personalized search results in professional networks"):
  with members:
    - "personalized expertise search at linkedin"
    - "personalized federated search at linkedin"

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)