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: "relationship profiling over social networks reverse smoothness from similarity to closeness"
  Abstract: On social networks, while nodes bear rich attributes, we often lack the `semantics' of why each link is formed-- and thus we are missing the `road signs' to navigate and organize the complex social universe. How to identify relationship semantics without labels? Founded on the prevalent homophily principle, we propose the novel problem of Attribute-based Relationship Profiling (ARP), to profile the closeness w.r.t. the underlying relationships (e.g., schoolmate) between users based on their similarity in the corresponding attributes (e.g., education) and, as output, learn a set of social affinity graphs, where each link is weighted by its probabilities of carrying the relationships. As requirements, ARP should be systematic and complete to profile every link for every relationship-- our challenges lie in effectively modeling homophily. We propose a novel reverse smoothness principle by observing that the similarity-closeness duality of homophily is consistent with the well-known smoothness assumption in graph-based semi-supervised learning-- only the direction of inference is reversed. To realize smoothness over noisy social graphs, we further propose a novel holistic closeness modeling approach to capture `high-order' smoothness by extending closeness from edges to paths. Extensive experiments on three real-world datasets demonstrate the efficacy of ARP.

Connected through hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "relation learning on social networks with multi modal graph edge variational autoencoders"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "place deduplication with embeddings"
    - "query specific knowledge summarization with entity evolutionary networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"

Connected through hyperedge HE-2 (induced by source paper "relation learning on social networks with multi modal graph edge variational autoencoders"):
  with members:
    - "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "representation learning on graphs with jumping knowledge networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "metapad meta pattern discovery from massive text corpora"
    - "topological recurrent neural network for diffusion prediction"

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)