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: "learning from history and present next item recommendation via discriminatively exploiting user behaviors"
  Abstract: In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendationsare becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.

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 "sdm sequential deep matching model for online large scale recommender system"):
  with members:
    - "a long short demands aware model for next item recommendation"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "deep session interest network for click through rate prediction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "next item recommendation with self attention"
    - "a simple convolutional generative network for next item recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-3 (induced by source paper "explainable fashion recommendation a semantic attribute region guided approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explainable recommendation a survey and new perspectives"
    - "visually explainable recommendation"
    - "aesthetic based clothing recommendation"
    - "a hierarchical attention model for social contextual image recommendation"
    - "visually aware fashion recommendation and design with generative image models"
    - "sherlock sparse hierarchical embeddings for visually aware one class collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "deep session interest network for click through rate prediction"):
  with members:
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"

Connected through hyperedge HE-5 (induced by source paper "binarized collaborative filtering with distilling graph convolutional networks"):
  with members:
    - "ranking distillation learning compact ranking models with high performance for recommender system"
    - "towards binary valued gates for robust lstm training"
    - "large scale learnable graph convolutional networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "deep cross network for ad click predictions"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-6 (induced by source paper "promotion of answer value measurement with domain effects in community question answering systems"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "convolutional neural network architectures for matching natural language sentences"
    - "word embedding based correlation model for question answer matching"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "speech recognition with deep recurrent neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "collaborative metric learning with memory network for multi relational recommender systems"):
  with members:
    - "topic enhanced memory networks for personalised point of interest recommendation"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "evaluation of session based recommendation algorithms"):
  with members:
    - "sequence aware recommender systems"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "session based recommendations with recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"

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)