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: "improved topological approximations by digitization"
  Abstract: Cech complexes are useful simplicial complexes for computing and analyzing topological features of data that lies in Euclidean space. Unfortunately, computing these complexes becomes prohibitively expensive for large-sized data sets even for medium-to-low dimensional data. We present an approximation scheme for $(1+\epsilon)$-approximating the topological information of the Cech complexes for $n$ points in $\mathbb{R}^d$, for $\epsilon\in(0,1]$. Our approximation has a total size of $n\left(\frac{1}{\epsilon}\right)^{O(d)}$ for constant dimension $d$, improving all the currently available $(1+\epsilon)$-approximation schemes of simplicial filtrations in Euclidean space. Perhaps counter-intuitively, we arrive at our result by adding additional $n\left(\frac{1}{\epsilon}\right)^{O(d)}$ sample points to the input. We achieve a bound that is independent of the spread of the point set by pre-identifying the scales at which the Cech complexes changes and sampling accordingly.

Connected through hyperedge HE-1 (induced by source paper "topological autoencoders"):
  with members:
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "graph filtration learning"
    - "homology preserving dimensionality reduction via manifold landmarking and tearing"
    - "on characterizing the capacity of neural networks using algebraic topology"
    - "adam a method for stochastic optimization"
    - "linear size approximations to the vietoris rips filtration"

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)