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: "multi behavioral sequential prediction with recurrent log bilinear model"
  Abstract: With the rapid growth of Internet applications, sequential prediction in collaborative filtering has become an emerging and crucial task. Given the behavioral history of a specific user, predicting his or her next choice plays a key role in improving various online services. Meanwhile, there are more and more scenarios with multiple types of behaviors, while existing works mainly study sequences with a single type of behavior. As a widely used approach, Markov chain based models are based on a strong independence assumption. As two classical neural network methods for modeling sequences, recurrent neural networks cannot well model short-term contexts, and the log-bilinear model is not suitable for long-term contexts. In this paper, we propose a Recurrent Log-BiLinear (RLBL) model. It can model multiple types of behaviors in historical sequences with behavior-specific transition matrices. RLBL applies a recurrent structure for modeling long-term contexts. It models several items in each hidden layer and employs position-specific transition matrices for modeling short-term contexts. Moreover, considering continuous time difference in behavioral history is a key factor for dynamic prediction, we further extend RLBL and replace position-specific transition matrices with time-specific transition matrices, and accordingly propose a Time-Aware Recurrent Log-BiLinear (TA-RLBL) model. Experimental results show that the proposed RLBL model and TA-RLBL model yield significant improvements over the competitive compared methods on three datasets, i.e., Movielens-1M dataset, Global Terrorism Database and Tmall dataset with different numbers of behavior types.

Connected through hyperedge HE-1 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations"):
  with members:
    - "session based recommendation with graph neural network"
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "learning to recommend with multiple cascading behaviors"
    - "research commentary on recommendations with side information a survey and research directions"
    - "improving end to end sequential recommendations with intent aware diversification"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a survey on session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "ekt exercise aware knowledge tracing for student performance prediction"):
  with members:
    - "transcribing content from structural images with spotlight mechanism"
    - "stable prediction across unknown environments"
    - "a survey on network embedding"
    - "dynamic memory networks for visual and textual question answering"
    - "dynamic key value memory networks for knowledge tracing"
    - "end to end memory networks"
    - "lstm based deep learning models for non factoid answer selection"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-3 (induced by source paper "mining significant microblogs for misinformation identification an attention based approach"):
  with members:
    - "attention based convolutional neural network for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "context aware sequential recommendation"
    - "gated attention readers for text comprehension"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "ask attend and answer exploring question guided spatial attention for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical contextual attention based gru network for sequential recommendation"):
  with members:
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "a visual and textual recurrent neural network for sequential prediction"
    - "context aware sequential recommendation"
    - "mv rnn a multi view recurrent neural network for sequential recommendation"
    - "crafting gbd net for object detection"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "effective approaches to attention based neural machine translation"
    - "reasoning about entailment with neural attention"

Connected through hyperedge HE-5 (induced by source paper "mv rnn a multi view recurrent neural network for sequential recommendation"):
  with members:
    - "a survey of multi view representation learning"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "inferring networks of substitutable and complementary products"
    - "session based recommendations with recurrent neural networks"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "gated feedback recurrent neural networks"
    - "going deeper with convolutions"

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)