human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "counterfactual learning from logs for improved ranking of e commerce products"):
  Abstract: Improved search quality enhances users' satisfaction, which directly impacts sales growth of an E-Commerce (E-Com) platform. Learning to Rank (LTR) algorithms require relevance judgments on products for learning. In real commercial scenarios, getting such judgments poses an immense challenge in application of LTR algorithms. In the literature, it is proposed to employ user feedback signals such as clicks, orders etc to generate relevance judgments. It is done by aggregating the logged data and calculating click rate, order rate etc of products, for each query in the logs. In this paper, we advocate counterfactual risk minimization (CRM) approach which circumvents the need of such data pre-processing and is better suited for learning from logged data, i.e. contextual bandit feedback. Due to unavailability of public E-Com LTR dataset, we provide Mercateo dataset from our E-Com platform. This dataset contains information of queries from real users, actions taken by the policy running on the system, probability of these actions and feedback of users on those actions. Our commercial dataset contains more than 10 million click log entries and 1 million order logs from a catalogue of about 3.5 million products and 3000 queries. To the best of our knowledge, this is the first work which examines effectiveness of CRM approach in learning ranking model from real-world logged data. Our empirical evaluation shows that CRM approach is able to learn directly from logged contextual-bandit feedback. Our method outperforms full-information loss on deep neural network model as well as traditional ranking models like LambdaMART. These findings have significant implications for improving the quality of search in E-Com platforms.

Includes members:
  Member "matchzoo a learning practicing and developing system for neural text matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning contextualized document representations for healthcare answer retrieval")
  Member "reinforcement learning to rank in e commerce search engine formalization analysis and application":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online similarity learning with feedback for invoice line item matching")
  Member "deeprank a new deep architecture for relevance ranking in information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "on application of learning to rank for e commerce search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "handling position bias for unbiased learning to rank in hotels search")
  Member "a deep relevance matching model for ad hoc retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "learning to match using local and distributed representations of text for web search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "a deep architecture for semantic matching with multiple positional sentence representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)