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 an optimal distributed algorithm for maximal independent set selection"
  Abstract: Maximal Independent Set selection is a fundamental problem in distributed computing. A novel probabilistic algorithm for this problem has recently been proposed by Afek et al, inspired by the study of the way that developing cells in the fly become specialised. The algorithm they propose is simple and robust, but not as efficient as previous approaches: the expected time complexity is O(log^2 n). Here we first show that the approach of Afek et al cannot achieve better efficiency than this across all networks, no matter how the probability values are chosen. However, we then propose a new algorithm that incorporates another important feature of the biological system: adapting the probabilities used at each node based on local feedback from neighbouring nodes. Our new algorithm retains all the advantages of simplicity and robustness, but also achieves the optimal efficiency of O(log n) expected time.

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 "beeping a maximal independent set fast"):
  with members:
    - "ant inspired density estimation via random walks"
    - "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"

Connected through hyperedge HE-3 (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"
    - "local computation lower and upper bounds"
    - "deploying wireless networks with beeps"
    - "the forgiving graph a distributed data structure for low stretch under adversarial attack"

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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "counting in one hop beeping networks"):
  with members:
    - "randomized communication in radio networks"
    - "design patterns in beeping algorithms"
    - "scalable wake up of multi channel single hop radio networks"
    - "design patterns in beeping algorithms examples emulation and analysis"
    - "the computational power of beeps"
    - "naming and counting in anonymous unknown dynamic networks"
    - "beeping a maximal independent set"
    - "deploying wireless networks with beeps"

Connected through hyperedge HE-6 (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"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "the computational power of beeps"
    - "randomized broadcast in radio networks with collision detection"
    - "the locality of distributed symmetry breaking"
    - "beeping a maximal independent set"
    - "deploying wireless networks with beeps"

Connected through hyperedge HE-7 (induced by source paper "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"):
  with members:
    - "the locality of distributed symmetry breaking"
    - "beeping a maximal independent set"
    - "deterministic distributed vertex coloring in polylogarithmic time"
    - "local computation lower and upper bounds"
    - "deploying wireless networks with beeps"
    - "distributed delta 1 coloring in linear in delta time"
    - "knowledge and common knowledge in a distributed environment"

Connected through hyperedge HE-8 (induced by source paper "the computational power of beeps"):
  with members:
    - "trade offs between selection complexity and performance when searching the plane without communication"
    - "beeping a maximal independent set"
    - "deploying wireless networks with beeps"
    - "the dynamics of probabilistic population protocols"
    - "the computational power of population protocols"

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)