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: "deploying wireless networks with beeps"
  Abstract: We present the \emph{discrete beeping} communication model, which assumes nodes have minimal knowledge about their environment and severely limited communication capabilities. Specifically, nodes have no information regarding the local or global structure of the network, don't have access to synchronized clocks and are woken up by an adversary. Moreover, instead on communicating through messages they rely solely on carrier sensing to exchange information. We study the problem of \emph{interval coloring}, a variant of vertex coloring specially suited for the studied beeping model. Given a set of resources, the goal of interval coloring is to assign every node a large contiguous fraction of the resources, such that neighboring nodes share no resources. To highlight the importance of the discreteness of the model, we contrast it against a continuous variant described in [17]. We present an O(1$ time algorithm that terminates with probability 1 and assigns an interval of size $\Omega(T/\Delta)$ that repeats every $T$ time units to every node of the network. This improves an $O(\log n)$ time algorithm with the same guarantees presented in \cite{infocom09}, and accentuates the unrealistic assumptions of the continuous model. Under the more realistic discrete model, we present a Las Vegas algorithm that solves $\Omega(T/\Delta)$-interval coloring in $O(\log n)$ time with high probability and describe how to adapt the algorithm for dynamic networks where nodes may join or leave. For constant degree graphs we prove a lower bound of $\Omega(\log n)$ on the time required to solve interval coloring for this model against randomized algorithms. This lower bound implies that our algorithm is asymptotically optimal for constant degree graphs.

Connected through hyperedge HE-1 (induced by source paper "distributed voting in beep model"):
  with members:
    - "distributed voting ranking with optimal number of states per node"
    - "beeping a maximal independent set fast"
    - "deterministic rendezvous with detection using beeps"
    - "counting in one hop beeping networks"
    - "naming a channel with beeps"
    - "asynchronous broadcasting with bivalent beeps"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "design patterns in beeping algorithms examples emulation and analysis"

Connected through hyperedge HE-2 (induced by source paper "noisy beeping networks"):
  with members:
    - "leader election in multi hop radio networks"
    - "efficient multiparty interactive coding for insertions deletions and substitutions"
    - "erasure correction for noisy radio networks"
    - "broadcasting in noisy radio networks"
    - "naming a channel with beeps"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "design patterns in beeping algorithms examples emulation and analysis"
    - "communicating with beeps"

Connected through hyperedge HE-3 (induced by source paper "leader election in multi hop radio networks"):
  with members:
    - "communicating with beeps"
    - "near optimal leader election in multi hop radio networks"

Connected through hyperedge HE-4 (induced by source paper "deterministic digital clustering of wireless ad hoc networks"):
  with members:
    - "a local broadcast layer for the sinr network model"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "dynamic networks of finite state machines"):
  with members:
    - "an improved distributed algorithm for maximal independent set"
    - "the locality of distributed symmetry breaking"
    - "beeping a maximal independent set"
    - "feedback from nature an optimal distributed algorithm for maximal independent set selection"
    - "local computation lower and upper bounds"
    - "the forgiving graph a distributed data structure for low stretch under adversarial attack"

Connected through hyperedge HE-6 (induced by source paper "deterministic rendezvous with detection using beeps"):
  with members:
    - "the computational power of beeps"
    - "time versus cost tradeoffs for deterministic rendezvous in networks"
    - "rendezvous of two robots with constant memory"
    - "how to meet asynchronously at polynomial cost"
    - "beeping a maximal independent set"
    - "delays induce an exponential memory gap for rendezvous in trees"

Connected through hyperedge HE-7 (induced by source paper "deviator detection under imperfect monitoring"):
  with members:
    - "computer science and game theory a brief survey"

Connected through hyperedge HE-8 (induced by source paper "naming a channel with beeps"):
  with members:
    - "randomized communication in radio networks"
    - "asynchronous broadcasting with bivalent beeps"
    - "scalable wake up of multi channel single hop radio networks"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "the computational power of beeps"
    - "anonymous processors with synchronous shared memory"
    - "communicating with beeps"
    - "randomized broadcast in radio networks with collision detection"

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)