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: "topology recognition with advice"
  Abstract: In topology recognition, each node of an anonymous network has to deterministically produce an isomorphic copy of the underlying graph, with all ports correctly marked. This task is usually unfeasible without any a priori information. Such information can be provided to nodes as advice. An oracle knowing the network can give a (possibly different) string of bits to each node, and all nodes must reconstruct the network using this advice, after a given number of rounds of communication. During each round each node can exchange arbitrary messages with all its neighbors and perform arbitrary local computations. The time of completing topology recognition is the number of rounds it takes, and the size of advice is the maximum length of a string given to nodes. #R##N#We investigate tradeoffs between the time in which topology recognition is accomplished and the minimum size of advice that has to be given to nodes. We provide upper and lower bounds on the minimum size of advice that is sufficient to perform topology recognition in a given time, in the class of all graphs of size $n$ and diameter $D\le \alpha n$, for any constant $\alpha< 1$. In most cases, our bounds are asymptotically tight.

Connected through hyperedge HE-1 (induced by source paper "advice complexity of treasure hunt in geometric terrains"):
  with members:
    - "the graph exploration problem with advice"
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "deterministic graph exploration with advice"
    - "tradeoffs between cost and information for rendezvous and treasure hunt"

Connected through hyperedge HE-2 (induced by source paper "cost vs information tradeoffs for treasure hunt in the plane"):
  with members:
    - "information complexity of treasure hunt in geometric terrains"
    - "the graph exploration problem with advice"
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "deterministic graph exploration with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "tradeoffs between cost and information for rendezvous and treasure hunt"

Connected through hyperedge HE-3 (induced by source paper "information complexity of treasure hunt in geometric terrains"):
  with members:
    - "the graph exploration problem with advice"
    - "reaching a target in the plane with no information"
    - "impact of knowledge on election time in anonymous networks"
    - "deterministic graph exploration with advice"
    - "time vs information tradeoffs for leader election in anonymous trees"

Connected through hyperedge HE-4 (induced by source paper "finding the size of a radio network with short labels"):
  with members:
    - "short labeling schemes for topology recognition in wireless tree networks"
    - "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "randomized broadcast in radio networks with collision detection"

Connected through hyperedge HE-5 (induced by source paper "constant length labeling schemes for deterministic radio broadcast"):
  with members:
    - "short labeling schemes for topology recognition in wireless tree networks"
    - "finding the size of a radio network with short labels"
    - "time vs information tradeoffs for leader election in anonymous trees"

Connected through hyperedge HE-6 (induced by source paper "short labeling schemes for topology recognition in wireless tree networks"):
  with members:
    - "time vs information tradeoffs for leader election in anonymous trees"

Connected through hyperedge HE-7 (induced by source paper "impact of knowledge on election time in anonymous networks"):
  with members:
    - "deterministic leader election in o d log n time with messages of size o 1"
    - "time vs information tradeoffs for leader election in anonymous trees"
    - "knowledge level of symmetry and time of leader election"
    - "election vs selection two ways of finding the largest node in a graph"
    - "leader election for anonymous asynchronous agents in arbitrary networks"

Connected through hyperedge HE-8 (induced by source paper "deterministic graph exploration with advice"):
  with members:
    - "impact of knowledge on election time in anonymous networks"
    - "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)