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: "modularity clustering is force directed layout"
  Abstract: Two natural and widely used representations for the community structure of networks are clusterings, which partition the vertex set into disjoint subsets, and layouts, which assign the vertices to positions in a metric space. This paper unifies prominent characterizations of layout quality and clustering quality, by showing that energy models of pairwise attraction and repulsion subsume Newman and Girvan's modularity measure. Layouts with optimal energy are relaxations of, and are thus consistent with, clusterings with optimal modularity, which is of practical relevance because the two representations are complementary and often used together.

Connected through hyperedge HE-1 (induced by source paper "batchlayout a batch parallel force directed graph layout algorithm in shared memory"):
  with members:
    - "multi level tree based approach for interactive graph visualization with semantic zoom"
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "graph drawing by stochastic gradient descent"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a distributed multilevel force directed algorithm"
    - "drawing large graphs by multilevel maxent stress optimization"

Connected through hyperedge HE-2 (induced by source paper "the twitter explorer a framework for observing twitter through interactive networks"):
  with members:
    - "forman ricci curvature for hypergraphs"

Connected through hyperedge HE-3 (induced by source paper "spring electrical models for link prediction"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "kissing cuisines exploring worldwide culinary habits on the web"):
  with members:
    - "fetishizing food in digital age foodporn around the world"
    - "foodporn obesity patterns in culinary interactions"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "you tweet what you eat studying food consumption through twitter"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"
    - "api design for machine learning software experiences from the scikit learn project"
    - "adadelta an adaptive learning rate method"
    - "a unified approach to mapping and clustering of bibliometric networks"

Connected through hyperedge HE-5 (induced by source paper "quantifying controversy in social media"):
  with members:
    - "characterizing information diets of social media users"
    - "controversy and sentiment in online news"
    - "partisan sharing facebook evidence and societal consequences"
    - "social determinants of content selection in the age of mis information"
    - "data portraits connecting people of opposing views"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-6 (induced by source paper "efficient and reliable network tomography in heterogeneous networks using bittorrent broadcasts and clustering algorithms"):
  with members:
    - "mpich g2 a grid enabled implementation of the message passing interface"

Connected through hyperedge HE-7 (induced by source paper "a unified approach to mapping and clustering of bibliometric networks"):
  with members:
    - "the structure and dynamics of co citation clusters a multiple perspective co citation analysis"
    - "a comparison of two techniques for bibliometric mapping multidimensional scaling and vos"

Connected through hyperedge HE-8 (induced by source paper "gmap drawing graphs as maps"):
  with members:
    - "choosing colors for geometric graphs via color space embeddings"

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.DM(Discrete Mathematics)