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: "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
  Abstract: We propose distributed algorithms for two well-established problems that operate efficiently under extremely harsh conditions. Our algorithms achieve state-of-the-art performance in a simple and novel way. #R##N#Our algorithm for maximal independent set selection operates on a network of identical anonymous processors. The processor at each node has no prior information about the network. At each time step, each node can only broadcast a single bit to all its neighbours, or remain silent. Each node can detect whether one or more neighbours have broadcast, but cannot tell how many of its neighbours have broadcast, or which ones. We build on recent work of Afek et al. which was inspired by studying the development of a network of cells in the fruit fly~\cite{Afek2011a}. However we incorporate for the first time another important feature of the biological system: varying the probability value used at each node based on local feedback from neighbouring nodes. Given any $n$-node network, our algorithm achieves the optimal expected time complexity of $O(\log n)$ rounds and the optimal expected message complexity of $O(1)$ single-bit messages broadcast by each node.We also show that the previous approach, without feedback, cannot achieve better than $\Omega(\log^2 n)$ expected time complexity, whatever global scheme is used to choose the probabilities. #R##N#Our algorithm for distributed greedy colouring works under similar harsh conditions: each identical node has no prior information about the network, can only broadcast a single message to all neighbours at each time step representing a desired colour, and can only detect whether at least one neighbour has broadcast each colour value. We show that our algorithm has an expected time complexity of $O(\Delta+\log n)$, where $\Delta$ is the maximum degree of the network, and expected message complexity of $O(1)$ messages broadcast by each node.

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"
    - "design patterns in beeping algorithms examples emulation and analysis"
    - "the computational power of beeps"

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"
    - "design patterns in beeping algorithms examples emulation and analysis"
    - "communicating with beeps"
    - "the adversarial noise threshold for distributed protocols"

Connected through hyperedge HE-3 (induced by source paper "simple dynamic algorithms for maximal independent set and other problems"):
  with members:
    - "fully dynamic mis in uniformly sparse graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"
    - "on the hardness of partially dynamic graph problems and connections to diameter"
    - "an improved distributed algorithm for maximal independent set"
    - "the locality of distributed symmetry breaking"

Connected through hyperedge HE-4 (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"
    - "the computational power of beeps"
    - "anonymous processors with synchronous shared memory"
    - "communicating with beeps"
    - "randomized broadcast in radio networks with collision detection"
    - "beeping a maximal independent set"

Connected through hyperedge HE-5 (induced by source paper "design patterns in beeping algorithms examples emulation and analysis"):
  with members:
    - "randomized communication in radio networks"
    - "counting in one hop beeping networks"
    - "the computational power of beeps"
    - "randomized broadcast in radio networks with collision detection"
    - "the locality of distributed symmetry breaking"
    - "beeping a maximal independent set"
    - "feedback from nature an optimal distributed algorithm for maximal independent set selection"
    - "deploying wireless networks with beeps"

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)