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: "tree decompositions and social graphs"
  Abstract: Recent work has established that large informatics graphs such as social and information networks have non-trivial tree-like structure when viewed at moderate size scales. Here, we present results from the first detailed empirical evaluation of the use of tree decomposition (TD) heuristics for structure identification and extraction in social graphs. Although TDs have historically been used in structural graph theory and scientific computing, we show that---even with existing TD heuristics developed for those very different areas---TD methods can identify interesting structure in a wide range of realistic informatics graphs. Our main contributions are the following: we show that TD methods can identify structures that correlate strongly with the core-periphery structure of realistic networks, even when using simple greedy heuristics; we show that the peripheral bags of these TDs correlate well with low-conductance communities (when they exist) found using local spectral computations; and we show that several types of large-scale "ground-truth" communities, defined by demographic metadata on the nodes of the network, are well-localized in the large-scale and/or peripheral structures of the TDs. Our other main contributions are the following: we provide detailed empirical results for TD heuristics on toy and synthetic networks to establish a baseline to understand better the behavior of the heuristics on more complex real-world networks; and we prove a theorem providing formal justification for the intuition that the only two impediments to low-distortion hyperbolic embedding are high tree-width and long geodesic cycles. Our results suggest future directions for improved TD heuristics that are more appropriate for realistic social graphs.

Connected through hyperedge HE-1 (induced by source paper "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "gunrock a high performance graph processing library on the gpu"
    - "route planning in transportation networks"
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "simple fast and scalable reachability oracle"
    - "hub accelerator fast and exact shortest path computation in large social networks"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-2 (induced by source paper "redundancy free computation graphs for graph neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "growing better graphs with latent variable probabilistic graph grammars"):
  with members:
    - "the infinity mirror test for analyzing the robustness of graph generators"
    - "growing graphs from hyperedge replacement graph grammars"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "a complete anytime algorithm for treewidth"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-4 (induced by source paper "lasagne locality and structure aware graph node embedding"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "neural message passing for quantum chemistry"
    - "parallel local graph clustering"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "parallelizing word2vec in shared and distributed memory"

Connected through hyperedge HE-5 (induced by source paper "slimness of graphs"):
  with members:
    - "fast approximation and exact computation of negative curvature parameters of graphs"
    - "core congestion is inherent in hyperbolic networks"
    - "euclidean versus hyperbolic congestion in idealized versus experimental networks"

Connected through hyperedge HE-6 (induced by source paper "scalable betweenness centrality maximization via sampling"):
  with members:
    - "tri est counting local and global triangles in fully dynamic streams with fixed memory size"
    - "a divide and conquer algorithm for betweenness centrality"
    - "triadic measures on graphs the power of wedge sampling"
    - "axioms for centrality"
    - "maximizing social influence in nearly optimal time"
    - "shattering and compressing networks for centrality analysis"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-7 (induced by source paper "maximizing the strong triadic closure in split graphs and proper interval graphs"):
  with members:
    - "romantic partnerships and the dispersion of social ties a network analysis of relationship status on facebook"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-8 (induced by source paper "a decentralized approach to software defined networks sdns"):
  with members:
    - "a monte carlo approach to lifespan failure performance analysis of the network fabric in modular data centers"
    - "quality of experience qoe beyond quality of service qos as its baseline qoe at the interface of experience domains"
    - "towards wire speed platform agnostic control of openflow switches"
    - "smartpacket re distributing the routing intelligence among network components in sdns"
    - "millions of little minions using packets for low latency network programming and visibility extended version"
    - "a geometric distance oracle for large real world graphs"
    - "programming protocol independent packet processors"

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)