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 local broadcast layer for the sinr network model"
  Abstract: We present the first algorithm that implements an abstract MAC (absMAC) layer in the Signal-to-Interference-plus-Noise-Ratio (SINR) wireless network model. We first prove that efficient SINR implementations are not possible for the standard absMAC specification. We modify that specification to an "approximate" version that better suits the SINR model. We give an efficient algorithm to implement the modified specification, and use it to derive efficient algorithms for higher-level problems of global broadcast and consensus. #R##N#In particular, we show that the absMAC progress property has no efficient implementation in terms of the SINR strong connectivity graph $G_{1-\epsilon}$, which contains edges between nodes of distance at most $(1-\epsilon)$ times the transmission range, where $\epsilon>0$ is a small constant that can be chosen by the user. This progress property bounds the time until a node is guaranteed to receive some message when at least one of its neighbors is transmitting. #R##N#To overcome this limitation, we introduce the slightly weaker notion of approximate progress into the absMAC specification. We provide a fast implementation of the modified specification, based on decomposing a known algorithm into local and global parts. We analyze our algorithm in terms of local parameters such as node degrees, rather than global parameters such as the overall number of nodes. A key contribution is our demonstration that such a local analysis is possible even in the presence of global interference. #R##N#Our absMAC algorithm leads to several new, efficient algorithms for solving higher-level problems in the SINR model. Namely, by combining our algorithm with known high-level algorithms, we obtain an improved algorithm for global single-message broadcast in the SINR model, and the first efficient algorithm for multi-message broadcast in that model.

Connected through hyperedge HE-1 (induced by source paper "deterministic digital clustering of wireless ad hoc networks"):
  with members:
    - "how well can graphs represent wireless interference"
    - "batched point location in sinr diagrams via algebraic tools"
    - "on the impact of geometry on ad hoc communication in wireless networks"
    - "competitive mac under adversarial sinr"
    - "distributed deterministic broadcasting in uniform power ad hoc wireless networks"
    - "towards tight bounds for local broadcasting"
    - "a constant factor approximation for wireless capacity maximization with power control in the sinr model"
    - "deploying wireless networks with beeps"

Connected through hyperedge HE-2 (induced by source paper "beeping a maximal independent set fast"):
  with members:
    - "ant inspired density estimation via random walks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "data dissemination in unified dynamic wireless networks"):
  with members:
    - "leveraging multiple channels in ad hoc networks"
    - "distributed bare bones communication in wireless networks"
    - "distributed communication in bare bones wireless networks"
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "randomized broadcast in radio networks with collision detection"
    - "beyond geometry towards fully realistic wireless models"
    - "radio network lower bounds made easy"
    - "on the impact of geometry on ad hoc communication in wireless 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)