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 scalable null model for directed graphs matching all degree distributions in out and reciprocal"
  Abstract: Degree distributions are arguably the most important property of real world networks. The classic edge configuration model or Chung-Lu model can generate an undirected graph with any desired degree distribution. This serves as a good null model to compare algorithms or perform experimental studies. Furthermore, there are scalable algorithms that implement these models and they are invaluable in the study of graphs. However, networks in the real-world are often directed, and have a significant proportion of reciprocal edges. A stronger relation exists between two nodes when they each point to one another (reciprocal edge) as compared to when only one points to the other (one-way edge). Despite their importance, reciprocal edges have been disregarded by most directed graph models. #R##N#We propose a null model for directed graphs inspired by the Chung-Lu model that matches the in-, out-, and reciprocal-degree distributions of the real graphs. Our algorithm is scalable and requires $O(m)$ random numbers to generate a graph with $m$ edges. We perform a series of experiments on real datasets and compare with existing graph models.

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (induced by source paper "provable and practical approximations for the degree distribution using sublinear graph samples"):
  with members:
    - "measuring and modeling bipartite graphs with community structure"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "approximately counting triangles in sublinear time"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "graph sample and hold a framework for big graph analytics"
    - "network sampling from static to streaming graphs"

Connected through hyperedge HE-3 (induced by source paper "measuring and modeling bipartite graphs with community structure"):
  with members:
    - "maximizing barber s bipartite modularity is also hard"
    - "a scalable generative graph model with community structure"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-4 (induced by source paper "a scalable generative graph model with community structure"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "an in depth analysis of stochastic kronecker graphs"
    - "decompositions of triangle dense graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "community structure and scale free collections of erdős renyi graphs"
    - "revisiting degree distribution models for social graph analysis"
    - "the similarity between stochastic kronecker and chung lu graph models"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-5 (induced by source paper "reciprocal versus parasocial relationships in online social networks"):
  with members:
    - "dynamics of trust reciprocation in heterogenous mmog networks"
    - "directed closure measures for networks with reciprocity"
    - "multi scale dynamics in a massive online social network"
    - "nonparametric link prediction in dynamic networks"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "predicting links and inferring attributes using a social attribute network san"
    - "four degrees of separation"

Connected through hyperedge HE-6 (induced by source paper "directed closure measures for networks with reciprocity"):
  with members:
    - "triadic measures on graphs the power of wedge sampling"
    - "friendship networks and social status"
    - "degree relations of triangles in real world networks and graph models"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-7 (induced by source paper "dynamics of trust reciprocation in heterogenous mmog networks"):
  with members:
    - "weighted reciprocity in human communication networks"
    - "twitter reciprocal reply networks exhibit assortativity with respect to happiness"

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.SI(Social and Information Networks)