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: "constructing and sampling graphs with a prescribed joint degree distribution"
  Abstract: One of the most influential recent results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance. We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative. #R##N#We suggest understanding the relationship between network structure and the joint degree distribution of graphs is an interesting avenue of further research. An important tool for such studies are algorithms that can generate random instances of graphs with the same joint degree distribution. This is the main topic of this paper and we study the problem from both a theoretical and practical perspective. We provide an algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them. We also show that the state space of simple graphs with a fixed degree distribution is connected via end point switches. We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge. These experiments show that our Markov Chain mixes quickly on real graphs, allowing for utilization of our techniques in practice.

Connected through hyperedge HE-1 (induced by source paper "common greedy wiring and rewiring heuristics do not guarantee maximum assortative graphs of given degree"):
  with members:
    - "quick detection of high degree entities in large directed networks"
    - "practical graph isomorphism ii"

Connected through hyperedge HE-2 (induced by source paper "graphic realizations of joint degree matrices"):
  with members:
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-3 (induced by source paper "evolution of social attribute networks measurements modeling and implications using google"):
  with members:
    - "google or google examining the popularity of the new osn"
    - "multi scale dynamics in a massive online social network"
    - "predicting links and inferring attributes using a social attribute network san"
    - "four degrees of separation"
    - "multiplicative attribute graph model of real world networks"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-4 (induced by source paper "2 5k graphs from sampling to generation"):
  with members:
    - "social structure of facebook networks"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "multigraph sampling of online social networks"
    - "estimating and sampling graphs with multidimensional random walks"
    - "exploring biological network structure with clustered random networks"
    - "systematic topology analysis and generation using degree correlations"

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)