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: "ant inspired density estimation via random walks"
  Abstract: Many ant species employ distributed population density estimation in applications ranging from quorum sensing [Pra05], to task allocation [Gor99], to appraisal of enemy colony strength [Ada90]. It has been shown that ants estimate density by tracking encounter rates -- the higher the population density, the more often the ants bump into each other [Pra05,GPT93]. #R##N#We study distributed density estimation from a theoretical perspective. We show that a group of anonymous agents randomly walking on a grid are able to estimate their density $d$ to within a multiplicative factor $1 \pm \epsilon$ with probability $1-\delta$ in just $\tilde O \left (\frac{\log(1/\delta)\log(1/d\epsilon)}{d\epsilon^2} \right )$ steps by measuring their encounter rates with other agents. Despite dependencies inherent in the fact that nearby agents may collide repeatedly (and, worse, cannot recognize when this happens), this bound nearly matches what is required to estimate $d$ by independently sampling grid locations. #R##N#From a biological perspective, our work helps shed light on how ants and other social insects can obtain relatively accurate density estimates via encounter rates. From a technical perspective, our analysis provides new tools for understanding complex dependencies in the collision probabilities of multiple random walks. We bound the strength of these dependencies using $local\ mixing\ properties$ of the underlying graph. Our results extend beyond the grid to more general graphs and we discuss applications to social network size estimation, density estimation by robot swarms, and random walked-based sampling of sensor networks.

Connected through hyperedge HE-1 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics"):
  with members:
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "the ants problem"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "distributed graph clustering and sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "a tight analysis of the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-2 (induced by source paper "degree ranking using local information"):
  with members:
    - "a faster method to estimate closeness centrality ranking"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"
    - "estimating the degree centrality ranking of a node"
    - "walk not wait faster sampling over online social networks"
    - "real time enhanced random sampling of online social networks"
    - "graph size estimation"

Connected through hyperedge HE-3 (induced by source paper "a distributed learning dynamics in social groups"):
  with members:
    - "human collective intelligence as distributed bayesian inference"
    - "irls and slime mold equivalence and convergence"
    - "distributed house hunting in ant colonies"
    - "stabilizing consensus with many opinions"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "decentralized online learning algorithms for opportunistic spectrum access"
    - "online distributed sensor selection"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-4 (induced by source paper "beeping a maximal independent set fast"):
  with members:
    - "a local broadcast layer for the sinr network model"
    - "an improved distributed algorithm for maximal independent set"
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "consensus with an abstract mac layer"
    - "the locality of distributed symmetry breaking"
    - "beeping a maximal independent set"
    - "feedback from nature an optimal distributed algorithm for maximal independent set selection"

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)