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: "a generalized cheeger inequality"
  Abstract: The generalized conductanceφ(G,H) between two graphsG and H on the same vertex set V is defined as the ratio φ(G,H) = min SV capG(S, ¯ S) capH(S, ¯ S) , where capG(S, ¯ S) is the total weight of the edges crossing from S to ¯ S = V S. We show that the minimum generalized eigenvalueλ(LG,LH) of the pair of Laplacians LG and LH satisfies λ(LG,LH) � φ(G,H)φ(G)/8, where φ(G) is the usual conductance of G. A generalized cut that meets this bound can be obtained from the generalized eigenvector corresponding to λ(LG,LH). The inequality complements a result of Trevisan (Tre13) which shows that φ(G) cannot be replaced by �(φ(G,H)) in the above inequality, unless the Unique Games Conjecture is false.

Connected through hyperedge HE-1 (induced by source paper "spectral algorithms for temporal graph cuts"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "on temporal graph exploration"
    - "active semi supervised learning using sampling theory for graph signals"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "is cheeger type approximation possible for nonuniform sparsest cut"
    - "a survey on multi view learning"
    - "sequential detection of temporal communities by estrangement confinement"

Connected through hyperedge HE-2 (induced by source paper "scalable constrained clustering a generalized spectral method"):
  with members:
    - "on constrained spectral clustering and its applications"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "social structure of facebook networks"
    - "a tutorial on spectral clustering"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "objective criteria for the evaluation of clustering methods"

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)