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: "recommender systems for the conference paper assignment problem"
  Abstract: Conference paper assignment, i.e., the task of assigning paper submissions to reviewers, presents multi-faceted issues for recommender systems research. Besides the traditional goal of predicting `who likes what?', a conference management system must take into account aspects such as: reviewer capacity constraints, adequate numbers of reviews for papers, expertise modeling, conflicts of interest, and an overall distribution of assignments that balances reviewer preferences with conference objectives. Among these, issues of modeling preferences and tastes in reviewing have traditionally been studied separately from the optimization of paper-reviewer assignment. In this paper, we present an integrated study of both these aspects. First, due to the paucity of data per reviewer or per paper (relative to other recommender systems applications) we show how we can integrate multiple sources of information to learn paper-reviewer preference models. Second, our models are evaluated not just in terms of prediction accuracy but in terms of the end-assignment quality. Using a linear programming-based assignment optimization formulation, we show how our approach better explores the space of unsupplied assignments to maximize the overall affinities of papers assigned to reviewers. We demonstrate our results on real reviewer preference data from the IEEE ICDM 2007 conference.

Connected through hyperedge HE-1 (induced by source paper "a multi label classification method using a hierarchical and transparent representation for paper reviewer recommendation"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "large scale multi label text classification revisiting neural networks"

Connected through hyperedge HE-2 (induced by source paper "paper matching with local fairness constraints"):
  with members:
    - "the conference paper assignment problem using order weighted averages to assign indivisible goods"
    - "a framework for optimizing paper matching"
    - "an algorithm to determine peer reviewers"

Connected through hyperedge HE-3 (induced by source paper "the conference paper assignment problem using order weighted averages to assign indivisible goods"):
  with members:
    - "properties of multiwinner voting rules"
    - "finding a collective set of items from proportional multirepresentation to group recommendation"
    - "computational aspects of multi winner approval voting"
    - "a framework for optimizing paper matching"

Connected through hyperedge HE-4 (induced by source paper "mobile multimedia recommendation in smart communities a survey"):
  with members:
    - "building smart communities with cyber physical systems"

Connected through hyperedge HE-5 (induced by source paper "active learning for matching problems"):
  with members:
    - "a framework for optimizing paper matching"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a framework for optimizing paper matching"):
  with members:
    - "exponential family graph matching and ranking"
    - "an algorithm to determine peer reviewers"

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)