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: "finding the size of a radio network with short labels"
  Abstract: The number of nodes of a network, called its size, is one of the most important network parameters. A radio network is a collection of stations, called nodes, with wireless transmission and receiving capabilities. It is modeled as a simple connected undirected graph whose nodes communicate in synchronous rounds. In each round, a node can either transmit a message to all its neighbors, or stay silent and listen. At the receiving end, a node $v$ hears a message from a neighbor $w$ in a given round, if $v$ listens in this round, and if $w$ is its only neighbor that transmits in this round. If $v$ listens in a round, and two or more neighbors of $v$ transmit in this round, a collision occurs at $v$. Two scenarios are considered in the literature: if nodes can distinguish collision from silence (the latter occurs when no neighbor transmits), we say that the network has the collision detection capability, otherwise there is no collision detection. #R##N#We consider the task of size discovery: finding the size of an unknown radio network with collision detection. All nodes have to output the size of the network, using a deterministic algorithm. Nodes have labels which are (not necessarily distinct) binary strings. The length of a labeling scheme is the largest length of a label. #R##N#Our main result states that the minimum length of a labeling scheme permitting size discovery in the class of networks of maximum degree Delta is Theta(\log\log Delta).

Connected through hyperedge HE-1 (induced by source paper "wireless broadcast with short labelling"):
  with members:
    - "markovian model for broadcast in wireless body area networks"
    - "ban gzkp optimal zero knowledge proof based scheme for wireless body area networks"
    - "peak transmission rate resilient crosslayer broadcast for body area networks"
    - "total order reliable convergecast in wban"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "ubiquitous healthcare in wireless body area networks a survey"

Connected through hyperedge HE-2 (induced by source paper "wireless broadcast with optimal labelling"):
  with members:
    - "ban gzkp optimal zero knowledge proof based scheme for wireless body area networks"
    - "peak transmission rate resilient crosslayer broadcast for body area networks"
    - "constant length labeling schemes for deterministic radio broadcast"
    - "total order reliable convergecast in wban"
    - "time vs information tradeoffs for leader election in anonymous trees"

Connected through hyperedge HE-3 (induced by source paper "constant length labeling schemes for deterministic radio broadcast"):
  with members:
    - "short labeling schemes for topology recognition in wireless tree networks"
    - "topology recognition with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"

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)