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: "similarity aware spectral sparsification by edge filtering"
  Abstract: In recent years, spectral graph sparsification techniques that can compute ultra-sparse graph proxies have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this paper proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to VLSI CAD, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications.

Connected through hyperedge HE-1 (induced by source paper "graphzoom a multi level spectral approach for accurate and scalable graph embedding"):
  with members:
    - "network embedding on compression and learning"
    - "initialization for network embedding a graph partition approach"
    - "learning topological representation for networks via hierarchical sampling"
    - "label efficient semi supervised learning via graph filtering"
    - "graph neural networks a review of methods and applications"
    - "mile a multi level framework for scalable graph embedding"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph embedding techniques applications and performance a survey"

Connected through hyperedge HE-2 (induced by source paper "grass spectral sparsification leveraging scalable spectral perturbation analysis"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "partitioning well clustered graphs spectral clustering works"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "a note on preconditioning by low stretch spanning trees"
    - "subgraph sparsification and nearly optimal ultrasparsifiers"

Connected through hyperedge HE-3 (induced by source paper "graspel graph spectral learning at scale"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "nearly linear time spectral graph reduction for scalable graph partitioning and data visualization"
    - "towards scalable spectral clustering via spectrum preserving sparsification"
    - "clustering with t sne provably"
    - "graph learning from data under laplacian and structural constraints"
    - "partitioning well clustered graphs spectral clustering works"
    - "learning laplacian matrix in smooth graph signal representations"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "towards scalable spectral sparsification of directed graphs"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"
    - "graph sparsification by effective resistances"

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.DS(Data Structures and Algorithms)