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: "online ranking with top 1 feedback"
  Abstract: We consider a setting where a system learns to rank a fixed set of $m$ items. The goal is produce good item rankings for users with diverse interests who interact online with the system for $T$ rounds. We consider a novel top-$1$ feedback model: at the end of each round, the relevance score for only the top ranked object is revealed. However, the performance of the system is judged on the entire ranked list. We provide a comprehensive set of results regarding learnability under this challenging setting. For PairwiseLoss and DCG, two popular ranking measures, we prove that the minimax regret is $\Theta(T^{2/3})$. Moreover, the minimax regret is achievable using an efficient strategy that only spends $O(m \log m)$ time per round. The same efficient strategy achieves $O(T^{2/3})$ regret for Precision@$k$. Surprisingly, we show that for normalized versions of these ranking measures, i.e., AUC, NDCG \& MAP, no online ranking algorithm can have sublinear regret.

Connected through hyperedge HE-1 (induced by source paper "counterfactual learning from logs for improved ranking of e commerce products"):
  with members:
    - "matchzoo a learning practicing and developing system for neural text matching"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "on application of learning to rank for e commerce search"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "higher order ranking and link prediction from closing triangles to closing higher order motifs"):
  with members:
    - "verse versatile graph embeddings from similarity measures"

Connected through hyperedge HE-3 (induced by source paper "weighted voting via no regret learning"):
  with members:
    - "weighted electoral control"
    - "common voting rules as maximum likelihood estimators"
    - "how hard is bribery in elections"

Connected through hyperedge HE-4 (induced by source paper "online learning to rank with feedback at the top"):
  with members:
    - "approximating matrix p norms"

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.LG(Machine Learning)