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 parallel algorithm for generating a random graph with a prescribed degree sequence"
  Abstract: Random graphs (or networks) have gained a significant increase of interest due to its popularity in modeling and simulating many complex real-world systems. Degree sequence is one of the most important aspects of these systems. Random graphs with a given degree sequence can capture many characteristics like dependent edges and non-binomial degree distribution that are absent in many classical random graph models such as the Erdős-Renyi graph model. In addition, they have important applications in the uniform sampling of random graphs, counting the number of graphs having the same degree sequence, as well as in string theory, random matrix theory, and matching theory. In this paper, we present an OpenMP-based shared-memory parallel algorithm for generating a random graph with a prescribed degree sequence, which achieves a speedup of 20.5 with 32 cores. One of the steps in our parallel algorithm requires checking the Erdős-Gallai characterization, i.e., whether there exists a graph obeying the given degree sequence, in parallel. This paper presents the first non-trivial parallel algorithm for checking the Erdős-Gallai characterization, which achieves a speedup of 23 using 32 cores.

Connected through hyperedge HE-1 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks"):
  with members:
    - "multi scale spectrum sensing in small cell mm wave cognitive wireless networks"
    - "understanding noise and interference regimes in 5g millimeter wave cellular networks"
    - "investigation of prediction accuracy sensitivity and parameter stability of large scale propagation path loss models for 5g wireless communications"
    - "gains of restricted secondary licensing in millimeter wave cellular systems"
    - "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"

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.DC(Distributed, Parallel, and Cluster Computing)