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: "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
  Abstract: We continue the line of research on graph compression started with WebGraph, but we move our focus to the compression of social networks in a proper sense (e.g., LiveJournal): the approaches that have been used for a long time to compress web graphs rely on a specific ordering of the nodes (lexicographical URL ordering) whose extension to general social networks is not trivial. In this paper, we propose a solution that mixes clusterings and orders, and devise a new algorithm, called Layered Label Propagation, that builds on previous work on scalable clustering and can be used to reorder very large graphs (billions of nodes). Our implementation uses overdecomposition to perform aggressively on multi-core architecture, making it possible to reorder graphs of more than 600 millions nodes in a few hours. Experiments performed on a wide array of web graphs and social networks show that combining the order produced by the proposed algorithm with the WebGraph compression framework provides a major increase in compression with respect to all currently known techniques, both on web graphs and on social networks. These improvements make it possible to analyse in main memory significantly larger graphs.

Connected through hyperedge HE-1 (induced by source paper "2ps high quality edge partitioning with two phase streaming"):
  with members:
    - "hype massive hypergraph partitioning with neighborhood expansion"
    - "scalable edge partitioning"
    - "a streaming algorithm for graph clustering"
    - "adwise adaptive window based streaming edge partitioning for high speed graph processing"
    - "bubing massive crawling for the masses"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "a closer look at lightweight graph reordering"):
  with members:
    - "domain specialized cache management for graph analytics"
    - "the gap benchmark suite"
    - "graphlab a new framework for parallel machine learning"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"

Connected through hyperedge HE-3 (induced by source paper "compressed data structures for binary relations in practice"):
  with members:
    - "compressed representation of dynamic binary relations with applications"
    - "simd compression and the intersection of sorted integers"
    - "compact binary relation representations with rich functionality"

Connected through hyperedge HE-4 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"):
  with members:
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "active access a mechanism for high performance distributed data centric computations"
    - "network accelerated non contiguous memory transfers"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "high level programming abstractions for distributed graph processing"

Connected through hyperedge HE-5 (induced by source paper "pytorch biggraph a large scale graph embedding system"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "mile a multi level framework for scalable graph embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "starspace embed all the things"
    - "predicting multicellular function through multi layer tissue networks"
    - "representation learning on graphs methods and applications"
    - "swivel improving embeddings by noticing what s missing"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "faster dynamic compressed d ary relations"):
  with members:
    - "compact and efficient representation of general graph databases"
    - "towards a compact representation of temporal rasters"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "aggregated 2d range queries on clustered points"
    - "fully functional static and dynamic succinct trees"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-7 (induced by source paper "single machine graph analytics on massive datasets using intel optane dc persistent memory"):
  with members:
    - "system evaluation of the intel optane byte addressable nvm"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "bigsparse high performance external graph analytics"
    - "early evaluation of intel optane non volatile memory with hpc i o workloads"
    - "the gap benchmark suite"

Connected through hyperedge HE-8 (induced by source paper "virtual web based personalized pagerank updating"):
  with members:
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "pagerank beyond the web"
    - "fast incremental and personalized pagerank"

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)