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: "force directed graph drawing using social gravity and scaling"
  Abstract: Force-directed layout algorithms produce graph drawings by resolving a system of emulated physical forces. We present techniques for using social gravity as an additional force in force-directed layouts, together with a scaling technique, to produce drawings of trees and forests, as well as more complex social networks. Social gravity assigns mass to vertices in proportion to their network centrality, which allows vertices that are more graph-theoretically central to be visualized in physically central locations. Scaling varies the gravitational force throughout the simulation, and reduces crossings relative to unscaled gravity. In addition to providing this algorithmic framework, we apply our algorithms to social networks produced by Mark Lombardi, and we show how social gravity can be incorporated into force-directed Lombardi-style drawings.

Connected through hyperedge HE-1 (induced by source paper "gravity inspired graph autoencoders for directed link prediction"):
  with members:
    - "a degeneracy framework for scalable graph autoencoders"
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "simplifying graph convolutional networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning to make predictions on graphs with autoencoders"

Connected through hyperedge HE-2 (induced by source paper "algorithmic extremism examining youtube s rabbit hole of radicalization"):
  with members:
    - "auditing radicalization pathways on youtube"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "spider and the flies focused crawling on tumblr to detect hate promoting communities"
    - "sentiment analysis on youtube a brief survey"

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.CG(Computational Geometry)