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: "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
  Abstract: Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across the two classes. For instance, modelling the connections between workers and their employers, or electors and parties they vote for, are examples of affiliation networks in social analysis. Ultimately, predicting interactions between products and consumers in personal recommendation systems and market models can provide priceless information for managing cyber-commerce. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Indeed, local state-of-the-art methods for link prediction do not directly exploit the inner bipartite topology, but rather rely on its projection into two one-mode-dimension networks, an example of which is the monopartite network of consumers connected by products and the monopartite network of products connected by consumers. Unfortunately, the one-mode-projections are always less informative than the original bipartite structure. Here, we overcome this theoretical obstacle, and we present a formal definition of common neighbour index (CN) and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilizes the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks, and represent the first attempt to create a local-based formalism that allows to intuitively implement link prediction fully in the bipartite domain.

Connected through hyperedge HE-1 (induced by source paper "bipartite link prediction based on topological features via 2 hop path"):
  with members:
    - "rwr gae random walk regularization for graph auto encoders"
    - "a degeneracy framework for scalable graph autoencoders"
    - "keep it simple graph autoencoders without graph convolutional networks"
    - "bipartite graph neural networks for efficient node representation learning"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-2 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links"):
  with members:
    - "lightweight mobile ad hoc network routing protocols for smartphones"
    - "impact of indirect contacts in emerging infectious disease on social networks"
    - "least cost influence maximization across multiple social networks"
    - "diffusion in colocation contact networks the impact of nodal spatiotemporal dynamics"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "latent self exciting point process model for spatial temporal networks"
    - "time varying graphs and dynamic networks"
    - "information propagation speed in mobile and delay tolerant networks"

Connected through hyperedge HE-3 (induced by source paper "balance in signed bipartite networks"):
  with members:
    - "butterfly counting in bipartite networks"
    - "signed network modeling based on structural balance theory"
    - "sne signed network embedding"
    - "measuring and modeling bipartite graphs with community structure"
    - "brazilian congress structural balance analysis"
    - "a survey of signed network mining in social media"
    - "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications"
    - "the link prediction problem in bipartite networks"

Connected through hyperedge HE-4 (induced by source paper "temporal similarity metrics for latent network reconstruction the role of time lag decay"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "identifying top football players and springboard clubs from a football player collaboration and club transfer networks"
    - "the role of social networks in information diffusion"
    - "inferring networks of diffusion and influence"
    - "supervised random walks predicting and recommending links in social networks"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-5 (induced by source paper "frnet dti deep convolutional neural networks with evolutionary and structural features for drug target interaction"):
  with members:
    - "idti esboost identification of drug target interaction using evolutionary and structural features with boosting"
    - "meboost mixing estimators with boosting for imbalanced data classification"
    - "cusboost cluster based under sampling with boosting for imbalanced classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "network in network"

Connected through hyperedge HE-6 (induced by source paper "idti esboost identification of drug target interaction using evolutionary and structural features with boosting"):
  with members:
    - "smote synthetic minority over sampling technique"

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.SI(Social and Information Networks)