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: "algorithmic complexity of power law networks"
  Abstract: It was experimentally observed that the majority of real-world networks follow power law degree distribution. The aim of this paper is to study the algorithmic complexity of such "typical" networks. The contribution of this work is twofold. #R##N#First, we define a deterministic condition for checking whether a graph has a power law degree distribution and experimentally validate it on real-world networks. This definition allows us to derive interesting properties of power law networks. We observe that for exponents of the degree distribution in the range $[1,2]$ such networks exhibit double power law phenomenon that was observed for several real-world networks. Our observation indicates that this phenomenon could be explained by just pure graph theoretical properties. #R##N#The second aim of our work is to give a novel theoretical explanation why many algorithms run faster on real-world data than what is predicted by algorithmic worst-case analysis. We show how to exploit the power law degree distribution to design faster algorithms for a number of classical P-time problems including transitive closure, maximum matching, determinant, PageRank and matrix inverse. Moreover, we deal with the problems of counting triangles and finding maximum clique. Previously, it has been only shown that these problems can be solved very efficiently on power law graphs when these graphs are random, e.g., drawn at random from some distribution. However, it is unclear how to relate such a theoretical analysis to real-world graphs, which are fixed. Instead of that, we show that the randomness assumption can be replaced with a simple condition on the degrees of adjacent vertices, which can be used to obtain similar results. As a result, in some range of power law exponents, we are able to solve the maximum clique problem in polynomial time, although in general power law networks the problem is NP-complete.

Connected through hyperedge HE-1 (induced by source paper "prsim sublinear time simrank computation on large power law graphs"):
  with members:
    - "probesim scalable single source and top k simrank computations on dynamic graphs"
    - "sling a near optimal index structure for simrank"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "approximate personalized pagerank on dynamic graphs"
    - "panther fast top k similarity search in large networks"
    - "efficient simrank computation via linearization"
    - "axiomatic ranking of network role similarity"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-2 (induced by source paper "distributed maximal independent set on scale free networks"):
  with members:
    - "fast distributed pagerank computation"
    - "an improved distributed algorithm for maximal independent set"
    - "the locality of distributed symmetry breaking"

Connected through hyperedge HE-3 (induced by source paper "finding induced subgraphs in scale free inhomogeneous random graphs"):
  with members:
    - "on the average case complexity of parameterized clique"

Connected through hyperedge HE-4 (induced by source paper "eigenvalue decay implies polynomial time learnability for neural networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "on the complexity of learning neural networks"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "sgd learns the conjugate kernel class of the network"
    - "on statistical learning via the lens of compression"
    - "reliably learning the relu in polynomial time"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "distribution specific hardness of learning neural networks"

Connected through hyperedge HE-5 (induced by source paper "why do cascade sizes follow a power law"):
  with members:
    - "there is something beyond the twitter network"
    - "do cascades recur"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "ultrametricity of information cascades"
    - "what stops social epidemics"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-6 (induced by source paper "approximating optimization problems using eas on scale free networks"):
  with members:
    - "one size fits all effectiveness of local search on structured data"
    - "greed is good for deterministic scale free networks"
    - "performance analysis of evolutionary algorithms for the minimum label spanning tree problem"
    - "multiplicative drift analysis"

Connected through hyperedge HE-7 (induced by source paper "an axiomatic and an average case analysis of algorithms and heuristics for metric properties of graphs"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "kadabra is an adaptive algorithm for betweenness via random approximation"
    - "snap a general purpose network analysis and graph mining library"
    - "fast and simple computation of top k closeness centralities"
    - "approximation and fixed parameter subquadratic algorithms for radius and diameter"
    - "networkit an interactive tool suite for high performance network analysis"
    - "into the square on the complexity of quadratic time solvable problems"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-8 (induced by source paper "sublinear time estimation of degree distribution moments the arboricity connection"):
  with members:
    - "sublinear time algorithms for counting star subgraphs with applications to join selectivity estimation"
    - "approximately counting triangles in sublinear time"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "why do simple algorithms for triangle enumeration work in the real world"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
    - "listing all maximal cliques in sparse graphs in near optimal time"

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)