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: "self organized segregation on the grid"
  Abstract: We consider an agent-based model with exponentially distributed waiting times in which two types of agents interact locally over a graph, and based on this interaction and on the value of a common intolerance threshold \(\tau \), decide whether to change their types. This is equivalent to a zero-temperature ising model with Glauber dynamics, an asynchronous cellular automaton with extended Moore neighborhoods, or a Schelling model of self-organized segregation in an open system, and has applications in the analysis of social and biological networks, and spin glasses systems. Some rigorous results were recently obtained in the theoretical computer science literature, and this work provides several extensions. We enlarge the intolerance interval leading to the expected formation of large segregated regions of agents of a single type from the known size \(\epsilon >0\) to size \(\approx 0.134\). Namely, we show that for \(0.433< \tau < 1/2\) (and by symmetry \(1/2<\tau <0.567\)), the expected size of the largest segregated region containing an arbitrary agent is exponential in the size of the neighborhood. We further extend the interval leading to expected large segregated regions to size \(\approx 0.312\) considering “almost segregated” regions, namely regions where the ratio of the number of agents of one type and the number of agents of the other type vanishes quickly as the size of the neighborhood grows. In this case, we show that for \(0.344 < \tau \le 0.433\) (and by symmetry for \(0.567 \le \tau <0.656\)) the expected size of the largest almost segregated region containing an arbitrary agent is exponential in the size of the neighborhood. This behavior is reminiscent of supercritical percolation, where small clusters of empty sites can be observed within any sufficiently large region of the occupied percolation cluster. The exponential bounds that we provide also imply that complete segregation, where agents of a single type cover the whole grid, does not occur with high probability for \(p=1/2\) and the range of intolerance considered.

Connected through hyperedge HE-1 (induced by source paper "a local stochastic algorithm for separation in heterogeneous self organizing particle systems"):
  with members:
    - "counting independent sets and colorings on random regular bipartite graphs"
    - "a stochastic approach to shortcut bridging in programmable matter"
    - "algorithms for bis hard problems on expander graphs"
    - "algorithmic pirogov sinai theory"
    - "line recovery by programmable particles"
    - "collaborative computation in self organizing particle systems"
    - "a markov chain algorithm for compression in self organizing particle systems"
    - "exponential segregation in a two dimensional schelling model with tolerant individuals"

Connected through hyperedge HE-2 (induced by source paper "improved intolerance intervals and size bounds for a schelling type spin system"):
  with members:
    - "minority population in the one dimensional schelling model of segregation"
    - "unperturbed schelling segregation in two or three dimensions"
    - "digital morphogenesis via schelling segregation"
    - "an analysis of one dimensional schelling segregation"

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)