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 "deep session interest network for click through rate prediction"):
  Abstract: Click-Through Rate (CTR) prediction plays an important role in many industrial applications, such as online advertising and recommender systems. How to capture users' dynamic and evolving interests from their behavior sequences remains a continuous research topic in the CTR prediction. However, most existing studies overlook the intrinsic structure of the sequences: the sequences are composed of sessions, where sessions are user behaviors separated by their occurring time. We observe that user behaviors are highly homogeneous in each session, and heterogeneous cross sessions. Based on this observation, we propose a novel CTR model named Deep Session Interest Network (DSIN) that leverages users' multiple historical sessions in their behavior sequences. We first use self-attention mechanism with bias encoding to extract users' interests in each session. Then we apply Bi-LSTM to model how users' interests evolve and interact among sessions. Finally, we employ the local activation unit to adaptively learn the influences of various session interests on the target item. Experiments are conducted on both advertising and production recommender datasets and DSIN outperforms other state-of-the-art models on both datasets.

Includes members:
  Member "learning from history and present next item recommendation via discriminatively exploiting user behaviors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations")
  Member "personalized top n sequential recommendation via convolutional sequence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in collaborative filtering and ranking")
  Member "self attentive sequential recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advances in collaborative filtering and ranking")
  Member "atrank an attention based user behavior modeling framework for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attentive item2vec neural attentive user representations")
  Member "deep cross network for ad click predictions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "deepfm a factorization machine based neural network for ctr prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "neural attentive session based recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attentive item2vec neural attentive user representations")
  Member "personalizing session based recommendations with hierarchical recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)