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: "distributed computation of persistent homology"
  Abstract: Persistent homology is a popular and powerful tool for capturing topological features of data. Advances in algorithms for computing persistent homology have reduced the computation time drastically -- as long as the algorithm does not exhaust the available memory. Following up on a recently presented parallel method for persistence computation on shared memory systems, we demonstrate that a simple adaption of the standard reduction algorithm leads to a variant for distributed systems. Our algorithmic design ensures that the data is distributed over the nodes without redundancy; this permits the computation of much larger instances than on a single machine. Moreover, we observe that the parallelism at least compensates for the overhead caused by communication between nodes, and often even speeds up the computation compared to sequential and even parallel shared memory algorithms. In our experiments, we were able to compute the persistent homology of filtrations with more than a billion (10^9) elements within seconds on a cluster with 32 nodes using less than 10GB of memory per node.

Connected through hyperedge HE-1 (induced by source paper "pi net a deep learning approach to extract topological persistence images"):
  with members:
    - "topology of learning in artificial neural networks"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "exposition and interpretation of the topology of neural networks"
    - "perturbation robust representations of topological persistence diagrams"
    - "quasi periodicity quantification in video data using topology"
    - "deep learning with topological signatures"
    - "persistent homology of attractors for action recognition"
    - "a taxonomy of deep convolutional neural nets for computer vision"

Connected through hyperedge HE-2 (induced by source paper "connectivity optimized representation learning via persistent homology"):
  with members:
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "a topological regularizer for classifiers via persistent homology"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "simba an efficient tool for approximating rips filtration persistence via simplicial batch collapse"
    - "ssd single shot multibox detector"
    - "densely connected convolutional networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-3 (induced by source paper "computing discrete morse complexes from simplicial complexes"):
  with members:
    - "the stellar tree a compact representation for simplicial complexes and beyond"
    - "building efficient and compact data structures for simplicial complexes"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "reducing complexes in multidimensional persistent homology theory"
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"
    - "graph induced complex on point data"
    - "random discrete morse theory and a new library of triangulations"

Connected through hyperedge HE-4 (induced by source paper "discrete morse theory for computing zigzag persistence"):
  with members:
    - "strong collapse for persistence"
    - "computing zigzag persistent cohomology"
    - "the compressed annotation matrix an efficient data structure for computing persistent cohomology"

Connected through hyperedge HE-5 (induced by source paper "persistent homology over directed acyclic graphs"):
  with members:
    - "applications of zigzag persistence to topological data analysis"
    - "computing multidimensional persistence"

Connected through hyperedge HE-6 (induced by source paper "singular persistent homology with effective concurrent computation"):
  with members:
    - "multicore homology via mayer vietoris"
    - "a spectral sequence for parallelized persistence"

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)