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: "translation based recommendation"
  Abstract: Modeling the complex interactions between users and items as well as amongst items themselves is at the core of designing successful recommender systems. One classical setting is predicting users' personalized sequential behavior (or 'next-item' recommendation), where the challenges mainly lie in modeling 'third-order' interactions between a user, her previously visited item(s), and the next item to consume. Existing methods typically decompose these higher-order interactions into a combination of pairwise relationships, by way of which user preferences (user-item interactions) and sequential patterns (item-item interactions) are captured by separate components. In this paper, we propose a unified method, TransRec, to model such third-order relationships for large-scale sequential prediction. Methodologically, we embed items into a 'transition space' where users are modeled as translation vectors operating on item sequences. Empirically, this approach outperforms the state-of-the-art on a wide spectrum of real-world datasets. Data and code are available at https://sites.google.com/a/eng.ucsd.edu/ruining-he/.

Connected through hyperedge HE-1 (induced by source paper "advances in collaborative filtering and ranking"):
  with members:
    - "temporal collaborative ranking via personalized transformer"
    - "graph dna deep neighborhood aware graph encoding for collaborative filtering"
    - "stochastic shared embeddings data driven regularization of embedding layers"
    - "hierarchical gating networks for sequential recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-2 (induced by source paper "fairrec two sided fairness for personalized recommendations in two sided platforms"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "incremental fairness in two sided market platforms on smoothly updating recommendations"
    - "estimating position bias without intrusive interventions"
    - "multi stakeholder recommendation and its connection to multi sided fairness"
    - "comparing approximate relaxations of envy freeness"
    - "equity of attention amortizing individual fairness in rankings"
    - "almost envy free allocations with connected bundles"
    - "fair division under cardinality constraints"

Connected through hyperedge HE-3 (induced by source paper "learning multi granular quantized embeddings for large vocab categorical features in recommender systems"):
  with members:
    - "albert a lite bert for self supervised learning of language representations"
    - "differentiable product quantization for end to end embedding compression"
    - "groupreduce block wise low rank approximation for neural language model shrinking"
    - "self attentive sequential recommendation"
    - "adaptive input representations for neural language modeling"
    - "neural collaborative filtering"
    - "learning k way d dimensional discrete code for compact embedding representations"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-4 (induced by source paper "relation embedding for personalised poi recommendation"):
  with members:
    - "fisher bures adversary graph convolutional networks"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "modeling relation paths for representation learning of knowledge bases"
    - "geo sage a geographical sparse additive generative model for spatial item recommendation"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (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-6 (induced by source paper "bert4rec sequential recommendation with bidirectional encoder representations from transformer"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "multi head attention with disagreement regularization"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"

Connected through hyperedge HE-7 (induced by source paper "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"):
  with members:
    - "signed distance based deep memory recommender"
    - "a hybrid approach to music playlist continuation based on playlist song membership"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "collaborative memory network for recommendation systems"
    - "gated attentive autoencoder for content aware recommendation"
    - "adversarial personalized ranking for recommendation"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "recurrent neural networks with top k gains for session based recommendations"

Connected through hyperedge HE-8 (induced by source paper "hierarchical gating networks for sequential recommendation"):
  with members:
    - "signed distance based deep memory recommender"
    - "regularizing matrix factorization with user and item embeddings for recommendation"
    - "gated attentive autoencoder for content aware recommendation"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "point of interest recommendation exploiting self attentive autoencoders with neighbor aware influence"
    - "a simple convolutional generative network for next item recommendation"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

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)