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: "predicting social links for new users across aligned heterogeneous social networks"
  Abstract: Online social networks have gained great success in recent years and many of them involve multiple kinds of nodes and complex relationships. Among these relationships, social links among users are of great importance. Many existing link prediction methods focus on predicting social links that will appear in the future among all users based upon a snapshot of the social network. In real-world social networks, many new users are joining in the service every day. Predicting links for new users are more important. Different from conventional link prediction problems, link prediction for new users are more challenging due to the following reasons: (1) differences in information distributions between new users and the existing active users (i.e., old users); (2) lack of information from the new users in the network. We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts. We proposed a within-target-network personalized sampling method to process the existing active users' information in order to accommodate the differences in information distributions before the intra-network knowledge transfer. SCAN-PS can also exploit information in other source networks, where the user accounts are aligned with the target network. In this way, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network.

Connected through hyperedge HE-1 (induced by source paper "from community to role based graph embeddings"):
  with members:
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "overlapping community detection with graph neural networks"
    - "feature propagation on graph a new perspective to graph representation learning"
    - "regal representation learning based graph alignment"
    - "graph embedding techniques applications and performance a survey"
    - "higher order graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "collective link prediction oriented network embedding with hierarchical graph attention"):
  with members:
    - "social network fusion and mining a survey"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"
    - "node2vec scalable feature learning for networks"
    - "gram graph based attention model for healthcare representation learning"
    - "a convolutional encoder model for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "representation learning for heterogeneous information networks via embedding events"):
  with members:
    - "heterogeneous information network embedding for meta path based proximity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "structural deep embedding for hyper networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "a survey of heterogeneous information network analysis"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "deep active learning for anchor user prediction"):
  with members:
    - "learning convolutional neural networks for graphs"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-5 (induced by source paper "meta diagram based active social networks alignment"):
  with members:
    - "active network alignment a matching based approach"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-6 (induced by source paper "social network fusion and mining a survey"):
  with members:
    - "bl ecd broad learning based enterprise community detection via hierarchical structure fusion"
    - "bl mne emerging heterogeneous social network embedding through broad learning with aligned autoencoder"
    - "bicycle sharing system analysis and trip prediction"
    - "node2vec scalable feature learning for networks"
    - "organizational chart inference"
    - "a survey of signed network mining in social media"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (induced by source paper "collaborative inference of coexisting information diffusions"):
  with members:
    - "inferring graphs from cascades a sparse recovery framework"
    - "diffusion and superposition distances for signals supported on networks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-8 (induced by source paper "world knowledge as indirect supervision for document clustering"):
  with members:
    - "lambda dependency based compositional semantics"
    - "selective transfer learning for cross domain recommendation"
    - "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.SI(Social and Information Networks)