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: "birank towards ranking on bipartite graphs"
  Abstract: The bipartite graph is a ubiquitous data structure that can model the relationship between two entity types: for instance, users and items, queries and webpages. In this paper, we study the problem of ranking vertices of a bipartite graph, based on the graph's link structure as well as prior information about vertices (which we term a query vector). We present a new solution, BiRank, which iteratively assigns scores to vertices and finally converges to a unique stationary ranking. In contrast to the traditional random walk-based methods, BiRank iterates towards optimizing a regularization function, which smooths the graph under the guidance of the query vector. Importantly, we establish how BiRank relates to the Bayesian methodology, enabling the future extension in a probabilistic way. To show the rationale and extendability of the ranking methodology, we further extend it to rank for the more generic n-partite graphs. BiRank's generic modeling of both the graph structure and vertex features enables it to model various ranking hypotheses flexibly. To illustrate its functionality, we apply the BiRank and TriRank (ranking for tripartite graphs) algorithms to two real-world applications: a general ranking scenario that predicts the future popularity of items, and a personalized ranking scenario that recommends items of interest to users. Extensive experiments on both synthetic and real-world datasets demonstrate BiRank's soundness (fast convergence), efficiency (linear in the number of graph edges) and effectiveness (achieving state-of-the-art in the two real-world tasks).

Connected through hyperedge HE-1 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph representation learning via hard and channel wise attention networks"
    - "towards better text understanding and retrieval through kernel entity salience modeling"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hypergraph neural networks"
    - "graph convolutional networks for text classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepinf social influence prediction with deep learning"

Connected through hyperedge HE-2 (induced by source paper "multi graph convolution collaborative filtering"):
  with members:
    - "feature generation by convolutional neural network for click through rate prediction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "a graph cnn for 3d point cloud classification"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "wide deep learning for recommender systems"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "neural graph collaborative filtering"):
  with members:
    - "relational collaborative filtering modeling multiple item relations for recommendation"
    - "kgat knowledge graph attention network for recommendation"
    - "unifying knowledge graph learning and recommendation towards a better understanding of user preferences"
    - "graph convolutional neural networks for web scale recommender systems"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "collaborative memory network for recommendation systems"
    - "representation learning on graphs with jumping knowledge networks"

Connected through hyperedge HE-4 (induced by source paper "learning vertex representations for bipartite networks"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a survey on network embedding"
    - "attributed network embedding for learning in a dynamic environment"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "node2vec scalable feature learning for networks"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "accumulative time based ranking method to reputation evaluation in information networks"):
  with members:
    - "measuring the academic reputation through citation networks via pagerank"
    - "information filtering via balanced diffusion on bipartite networks"
    - "a robust ranking algorithm to spamming"

Connected through hyperedge HE-6 (induced by source paper "addressing time bias in bipartite graph ranking for important node identification"):
  with members:
    - "attributed social network embedding"
    - "nais neural attentive item similarity model for recommendation"

Connected through hyperedge HE-7 (induced by source paper "attributed social network embedding"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "wide deep learning for recommender systems"
    - "bibliographic analysis with the citation network topic model"
    - "node2vec scalable feature learning for networks"
    - "latent space model for multi modal social data"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "nais neural attentive item similarity model for recommendation"):
  with members:
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "adversarial personalized ranking for recommendation"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "related pins at pinterest the evolution of a real world recommender system"

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)