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 "exact k recommendation via maximal clique optimization"):
  Abstract: This paper targets to a novel but practical recommendation problem named exact-K recommendation. It is different from traditional top-K recommendation, as it focuses more on (constrained) combinatorial optimization which will optimize to recommend a whole set of K items called card, rather than ranking optimization which assumes that "better" items should be put into top positions. Thus we take the first step to give a formal problem definition, and innovatively reduce it to Maximum Clique Optimization based on graph. To tackle this specific combinatorial optimization problem which is NP-hard, we propose Graph Attention Networks (GAttN) with a Multi-head Self-attention encoder and a decoder with attention mechanism. It can end-to-end learn the joint distribution of the K items and generate an optimal card rather than rank individual items by prediction scores. Then we propose Reinforcement Learning from Demonstrations (RLfD) which combines the advantages in behavior cloning and reinforcement learning, making it sufficient- and-efficient to train the model. Extensive experiments on three datasets demonstrate the effectiveness of our proposed GAttN with RLfD method, it outperforms several strong baselines with a relative improvement of 7.7% and 4.7% on average in Precision and Hit Ratio respectively, and achieves state-of-the-art (SOTA) performance for the exact-K recommendation problem.

Includes members:
  Member "a multi task selected learning approach for solving 3d flexible bin packing problem":
    also connected through other hyperedges:
      (none)
  Member "query tracking for e commerce conversational search a machine comprehension perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic search optimizing the game of information seeking")
  Member "learning a deep listwise context model for ranking refinement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "listwise learning to rank by exploring unique ratings")
  Member "automatic generation of chinese short product titles for mobile display":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi modal generative adversarial network for short product title generation in mobile e commerce")
  Member "representing verbs as argument concepts":
    also connected through other hyperedges:
      (none)
  Member "a brand level ranking system with the customized attention gru model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards neural mixture recommender for long range dependent user sequences")
  Member "deep cascade multi task learning for slot filling in online shopping assistant":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dialogact2vec towards end to end dialogue agent by multi task representation learning")
  Member "behavioral cloning from observation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")

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)