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: "selective transfer learning for cross domain recommendation"
  Abstract: Collaborative filtering (CF) aims to predict users' ratings on items according to historical user-item preference data. In many real-world applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring knowledge from some manually selected source domains, the data sparseness problem could be mitigated. However for most cases, parts of source domain data are not consistent with the observations in the target domain, which may misguide the target domain model building. In this paper, we propose a novel criterion based on empirical prediction error and its variance to better capture the consistency across domains in CF settings. Consequently, we embed this criterion into a boosting framework to perform selective knowledge transfer. Comparing to several state-of-the-art methods, we show that our proposed selective transfer learning framework can significantly improve the accuracy of rating prediction tasks on several real-world recommendation tasks.

Connected through hyperedge HE-1 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"

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"
    - "conet collaborative cross networks for cross domain recommendation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "transfer meets hybrid a synthetic approach for cross domain collaborative filtering with text"):
  with members:
    - "conet collaborative cross networks for cross domain recommendation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "conet collaborative cross networks for cross domain recommendation"):
  with members:
    - "collaborative filtering with topic and social latent factors incorporating implicit feedback"
    - "integrating reviews into personalized ranking for cold start recommendation"
    - "neural collaborative filtering"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "multi task self supervised visual learning"
    - "a survey on multi task learning"
    - "wide deep learning for recommender systems"
    - "cross stitch networks for multi task learning"

Connected through hyperedge HE-5 (induced by source paper "learn on source refine on target a model transfer learning framework with random forests"):
  with members:
    - "recognizing activities and spatial context using wearable sensors"
    - "learning from multiple outlooks"
    - "frustratingly easy domain adaptation"
    - "domain adaptation for statistical classifiers"
    - "a model of inductive bias learning"

Connected through hyperedge HE-6 (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"
    - "multi behavioral sequential prediction with recurrent log bilinear model"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a visual and textual recurrent neural network for sequential prediction"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "image based recommendations on styles and substitutes"
    - "going deeper with convolutions"
    - "sequential click prediction for sponsored search with recurrent neural networks"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-8 (induced by source paper "world knowledge as indirect supervision for document clustering"):
  with members:
    - "predicting social links for new users across aligned heterogeneous social networks"
    - "lambda dependency based compositional semantics"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "learning dependency based compositional semantics"
    - "wikipedia based semantic interpretation for natural language processing"

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.LG(Machine Learning)