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: "neural brane neural bayesian personalized ranking for attributed network embedding"
  Abstract: Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as, user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes; Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node-pair and a dissimilar node-pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods.

Connected through hyperedge HE-1 (induced by source paper "deep collaborative filtering with multi aspect information in heterogeneous networks"):
  with members:
    - "prsim sublinear time simrank computation on large power law graphs"
    - "deep learning based recommender system a survey and new perspectives"
    - "outer product based neural collaborative filtering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-2 (induced by source paper "self attentive neural collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "outer product based neural collaborative filtering"
    - "reasoning with sarcasm by reading in between"
    - "multi pointer co attention networks for recommendation"
    - "couplenet paying attention to couples with coupled attention for relationship recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"

Connected through hyperedge HE-3 (induced by source paper "hyperbolic recommender systems"):
  with members:
    - "hyperbolic attention networks"
    - "neurec on nonlinear transformation for personalized ranking"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "learning domain sensitive and sentiment aware word embeddings"):
  with members:
    - "name disambiguation in anonymized graphs using network embedding"
    - "neural semantic role labeling with dependency path embeddings"
    - "unsupervised cross domain word representation learning"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "thumbs up sentiment classification using machine learning techniques"

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)