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: "the simplex geometry of graphs"
  Abstract: Graphs are a central object of study in various scientific fields, such as discrete mathematics, theoretical computer science and network science. These graphs are typically studied using combinatorial, algebraic or probabilistic methods, each of which highlights the properties of graphs in a uniqueway. Here, we discuss a novel approach to study graphs: the simplex geometry (a simplex is a generalized triangle). This perspective, proposed by Miroslav Fiedler, introduces techniques from (simplex) geometry into the field of graph theory and conversely, via an exact correspondence. We introduce this graph-simplex correspondence, identify a number of basic connections between graph characteristics and simplex properties, and suggest some applications as example.

Connected through hyperedge HE-1 (induced by source paper "edge corona product as an approach to modeling complex simplical networks"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "scale free loopy structure is resistant to noise in consensus dynamics in complex networks"
    - "extended corona product as an exactly tractable model for weighted heterogeneous networks"
    - "maximum matchings and minimum dominating sets in apollonian networks and extended tower of hanoi graphs"
    - "deterministic hierarchical networks"

Connected through hyperedge HE-2 (induced by source paper "glee geometric laplacian eigenmap embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "representation learning on graphs methods and applications"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "node2vec scalable feature learning for networks"
    - "robustness and algebraic connectivity of random interdependent networks"
    - "deepwalk online learning of social representations"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-3 (induced by source paper "geometric laplacian eigenmap embedding"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "a tutorial on network embeddings"
    - "representation learning on graphs methods and applications"
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "node2vec scalable feature learning for networks"
    - "deepwalk online learning of social representations"

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)