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: "tight bounds for randomized load balancing on arbitrary network topologies"
  Abstract: We consider the problem of balancing load items (tokens) in networks. Starting with an arbitrary load distribution, we allow nodes to exchange tokens with their neighbors in each round. The goal is to achieve a distribution where all nodes have nearly the same number of tokens. #R##N#For the continuous case where tokens are arbitrarily divisible, most load balancing schemes correspond to Markov chains, whose convergence is fairly well-understood in terms of their spectral gap. However, in many applications, load items cannot be divided arbitrarily, and we need to deal with the discrete case where the load is composed of indivisible tokens. This discretization entails a non-linear behavior due to its rounding errors, which makes this analysis much harder than in the continuous case. #R##N#We investigate several randomized protocols for different communication models in the discrete case. As our main result, we prove that for any regular network in the matching model, all nodes have the same load up to an additive constant in (asymptotically) the same number of rounds as required in the continuous case. This generalizes and tightens the previous best result, which only holds for expander graphs, and demonstrates that there is almost no difference between the discrete and continuous cases. Our results also provide a positive answer to the question of how well discrete load balancing can be approximated by (continuous) Markov chains, which has been posed by many researchers.

Connected through hyperedge HE-1 (induced by source paper "parallel balanced allocations the heavily loaded case"):
  with members:
    - "the 1 2 3 toolkit for building your own balls into bins algorithm"
    - "balanced allocations a simple proof for the heavily loaded case"
    - "tight bounds for parallel randomized load balancing"

Connected through hyperedge HE-2 (induced by source paper "discrepancy analysis of a new randomized diffusion algorithm for weighted round matrices"):
  with members:
    - "improved analysis of deterministic load balancing schemes"
    - "deterministic random walks for rapidly mixing chains"
    - "quasirandom load balancing"

Connected through hyperedge HE-3 (induced by source paper "random walk based in network computation of arbitrary functions"):
  with members:
    - "decentralized random walk based data collection in networks"
    - "on coalescence time in graphs when is coalescing as fast as meeting"
    - "on the maximum rate of networked computation in a capacitated network"
    - "optimal embedding of functions for in network computation complexity analysis and algorithms"
    - "coalescing random walks and voting on connected graphs"
    - "towards a queueing based framework for in network function computation"

Connected through hyperedge HE-4 (induced by source paper "dispersion of mobile robots a study of memory time trade offs"):
  with members:
    - "efficient dispersion of mobile robots on graphs"
    - "deterministic dispersion of mobile robots in dynamic rings"
    - "a general lower bound for collaborative tree exploration"
    - "on the power of one bit how to explore a graph when you cannot backtrack"

Connected through hyperedge HE-5 (induced by source paper "population protocols for leader election and exact majority with o log 2 n states and o log 2 n convergence time"):
  with members:
    - "determining majority in networks with local interactions and very small local memory"
    - "coalescing random walks and voting on connected graphs"

Connected through hyperedge HE-6 (induced by source paper "distributed graph clustering by load balancing"):
  with members:
    - "communication optimal distributed clustering"
    - "find your place simple distributed algorithms for community detection"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "quasirandom load balancing"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

Connected through hyperedge HE-7 (induced by source paper "ergodic effects in token circulation"):
  with members:
    - "total variation discrepancy of deterministic random walks for ergodic markov chains"
    - "improved analysis of deterministic load balancing schemes"
    - "quasirandom load balancing"

Connected through hyperedge HE-8 (induced by source paper "plurality consensus via shuffling lessons learned from load balancing"):
  with members:
    - "stabilizing consensus with many opinions"
    - "plurality consensus in the gossip model"
    - "simple dynamics for plurality consensus"

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.DM(Discrete Mathematics)