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: "conet collaborative cross networks for cross domain recommendation"
  Abstract: The cross-domain recommendation technique is an effective way of alleviating the data sparse issue in recommender systems by leveraging the knowledge from relevant domains. Transfer learning is a class of algorithms underlying these techniques. In this paper, we propose a novel transfer learning approach for cross-domain recommendation by using neural networks as the base model. In contrast to the matrix factorization based cross-domain techniques, our method is deep transfer learning, which can learn complex user-item interaction relationships. We assume that hidden layers in two base networks are connected by cross mappings, leading to the collaborative cross networks (CoNet). CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa. CoNet is achieved in multi-layer feedforward networks by adding dual connections and joint loss functions, which can be trained efficiently by back-propagation. The proposed model is thoroughly evaluated on two large real-world datasets. It outperforms baselines by relative improvements of 7.84% in NDCG. We demonstrate the necessity of adaptively selecting representations to transfer. Our model can reduce tens of thousands training examples comparing with non-transfer methods and still has the competitive performance with them.

Connected through hyperedge HE-1 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation"):
  with members:
    - "future data helps training modeling future contexts for session based recommendation"
    - "parameter efficient transfer learning for nlp"
    - "albert a lite bert for self supervised learning of language representations"
    - "darec deep domain adaptation for cross domain recommendation via transferring rating patterns"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "vl bert pre training of generic visual linguistic representations"
    - "do better imagenet models transfer better"
    - "learning and transferring ids representation in e commerce"

Connected through hyperedge HE-2 (induced by source paper "parallel segregation integration networks for shared account cross domain sequential recommendations"):
  with members:
    - "sequence aware recommender systems"
    - "repeatnet a repeat aware neural recommendation machine for session based recommendation"
    - "cross domain recommendation via deep domain adaptation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "transfer meets hybrid a synthetic approach for cross domain collaborative filtering with text"):
  with members:
    - "collaborative memory network for recommendation systems"
    - "collaborative filtering with topic and social latent factors incorporating implicit feedback"
    - "lcmr local and centralized memories for collaborative filtering with unstructured text"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "a survey on multi task learning"
    - "transnets learning to transform for recommendation"

Connected through hyperedge HE-4 (induced by source paper "parallel split join networks for shared account cross domain sequential recommendations"):
  with members:
    - "adversarial personalized ranking for recommendation"
    - "cross domain recommendation via deep domain adaptation"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"

Connected through hyperedge HE-5 (induced by source paper "jscn joint spectral convolutional network for cross domain recommendation"):
  with members:
    - "deep cross networks with aesthetic preference for cross domain recommendation"
    - "deep learning based recommender system a survey and new perspectives"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "spectral collaborative filtering"
    - "neural collaborative filtering"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "cross stitch networks for multi task learning"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "sequential scenario specific meta learner for online recommendation"):
  with members:
    - "on first order meta learning algorithms"
    - "sequence aware recommender systems"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "one shot relational learning for knowledge graphs"
    - "neural collaborative filtering"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-7 (induced by source paper "dbrec dual bridging recommendation via discovering latent groups"):
  with members:
    - "mmalfm explainable recommendation by leveraging reviews and images"
    - "outer product based neural collaborative filtering"
    - "collaborative memory network for recommendation systems"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "what is the best way for extracting meaningful attributes from pictures"
    - "proje embedding projection for knowledge graph completion"

Connected through hyperedge HE-8 (induced by source paper "deep cross networks with aesthetic preference for cross domain recommendation"):
  with members:
    - "aesthetic based clothing recommendation"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"
    - "wide deep learning for recommender systems"
    - "cross stitch networks for multi task learning"
    - "brain inspired deep networks for image aesthetics assessment"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "adam a method for stochastic optimization"
    - "bpr bayesian personalized ranking from implicit feedback"

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)