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: "a sequential algorithm for generating random graphs"
  Abstract: We present a nearly-linear time algorithm for counting and randomly generating simple graphs with a given degree sequence in a certain range. For degree sequence (d i )i=1n with maximum degree d max =O(m 1/4−τ ), our algorithm generates almost uniform random graphs with that degree sequence in time O(md max ) where $m=\frac{1}{2}\sum_{i}d_{i}$is the number of edges in the graph and τ is any positive constant. The fastest known algorithm for uniform generation of these graphs (McKay and Wormald in J. Algorithms 11(1):52–67, 1990) has a running time of O(m 2 d max 2). Our method also gives an independent proof of McKay’s estimate (McKay in Ars Combinatoria A 19:15–25, 1985) for the number of such graphs.#R##N##R##N#We also use sequential importance sampling to derive fully Polynomial-time Randomized Approximation Schemes (FPRAS) for counting and uniformly generating random graphs for the same range of d max =O(m 1/4−τ ).#R##N##R##N#Moreover, we show that for d=O(n 1/2−τ ), our algorithm can generate an asymptotically uniform d-regular graph. Our results improve the previous bound of d=O(n 1/3−τ ) due to Kim and Vu (Adv. Math. 188:444–469, 2004) for regular graphs.

Connected through hyperedge HE-1 (induced by source paper "random features strengthen graph neural networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "kgat knowledge graph attention network for recommendation"
    - "graph convolutional networks with eigenpooling"
    - "graph u nets"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "what graph neural networks cannot learn depth vs width"

Connected through hyperedge HE-2 (induced by source paper "the switch markov chain for sampling irregular graphs and digraphs"):
  with members:
    - "expand and contract sampling graphs with given degrees and other combinatorial families"
    - "on uniform sampling simple directed graph realizations of degree sequences"

Connected through hyperedge HE-3 (induced by source paper "a parallel algorithm for generating a random graph with a prescribed degree sequence"):
  with members:
    - "parallel algorithms for generating random networks with given degree sequences"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-4 (induced by source paper "fault tolerant virtual backbone in heterogeneous wireless sensor network"):
  with members:
    - "approximation algorithm for minimum weight fault tolerant virtual backbone in unit disk graphs"

Connected through hyperedge HE-5 (induced by source paper "localization in seeded pagerank"):
  with members:
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "a scalable generative graph model with community structure"
    - "pagerank beyond the web"
    - "sublinear algorithms for local graph centrality estimation"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "quick detection of nodes with large degrees"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "strong localization in personalized pagerank vectors"):
  with members:
    - "heat kernel based community detection"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "quick detection of nodes with large degrees"

Connected through hyperedge HE-7 (induced by source paper "dimensionality of social networks using motifs and eigenvalues"):
  with members:
    - "a scalable generative graph model with community structure"
    - "social structure of facebook networks"
    - "multiplicative attribute graph model of real world networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "a convex formulation for learning scale free networks via submodular relaxation"):
  with members:
    - "optimization with sparsity inducing penalties"
    - "sparse inverse covariance selection via alternating linearization methods"
    - "structured sparsity inducing norms through submodular functions"
    - "convex analysis and optimization with submodular functions a tutorial"

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.CC(Computational Complexity)