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: "a new perspective on vertex connectivity"
  Abstract: Edge connectivity and vertex connectivity are two fundamental concepts in graph theory. Although by now there is a good understanding of the structure of graphs based on their edge connectivity, our knowledge in the case of vertex connectivity is much more limited. An essential tool in capturing edge connectivity are edge-disjoint spanning trees. The famous results of Tutte and Nash-Williams show that a graph with edge connectivity $\lambda$ contains $\floor{\lambda/2}$ edge-disjoint spanning trees. #R##N#We present connected dominating set (CDS) partition and packing as tools that are analogous to edge-disjoint spanning trees and that help us to better grasp the structure of graphs based on their vertex connectivity. The objective of the CDS partition problem is to partition the nodes of a graph into as many connected dominating sets as possible. The CDS packing problem is the corresponding fractional relaxation, where CDSs are allowed to overlap as long as this is compensated by assigning appropriate weights. CDS partition and CDS packing can be viewed as the counterparts of the well-studied edge-disjoint spanning trees, focusing on vertex disjointedness rather than edge disjointness. #R##N#We constructively show that every $k$-vertex-connected graph with $n$ nodes has a CDS packing of size $\Omega(k/\log n)$ and a CDS partition of size $\Omega(k/\log^5 n)$. We prove that the $\Omega(k/\log n)$ CDS packing bound is existentially optimal. #R##N#Using CDS packing, we show that if vertices of a $k$-vertex-connected graph are independently sampled with probability $p$, then the graph induced by the sampled vertices has vertex connectivity $\tilde{\Omega}(kp^2)$. Moreover, using our $\Omega(k/\log n)$ CDS packing, we get a store-and-forward broadcast algorithm with optimal throughput in the networking model where in each round, each node can send one bounded-size message to all its neighbors.

Connected through hyperedge HE-1 (induced by source paper "connectivity in interdependent networks"):
  with members:
    - "maximizing reliability in wdm networks through lightpath routing"
    - "distributed connectivity decomposition"

Connected through hyperedge HE-2 (induced by source paper "broadcasting in noisy radio networks"):
  with members:
    - "randomized broadcast in radio networks with collision detection"
    - "broadcast throughput in radio networks routing vs network coding"
    - "fast structuring of radio networks large for multi message communications"
    - "bounds on contention management in radio networks"
    - "analyzing network coding gossip made easy"
    - "on distributed computation in noisy random planar networks"

Connected through hyperedge HE-3 (induced by source paper "the cost of global broadcast in dynamic radio networks"):
  with members:
    - "multi message broadcast in dynamic radio networks"
    - "dynamic multiple message broadcast bounding throughput in the affectance model"
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "radio network lower bounds made easy"
    - "on the impact of geometry on ad hoc communication in wireless networks"
    - "distributed connectivity decomposition"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-4 (induced by source paper "on fast and robust information spreading in the vertex congest model"):
  with members:
    - "distributed connectivity decomposition"
    - "simple fast and deterministic gossip and rumor spreading"
    - "analyzing network coding gossip made easy"
    - "cover time and broadcast time"

Connected through hyperedge HE-5 (induced by source paper "approximation algorithms for connected maximum cut and related problems"):
  with members:
    - "analyzing the optimal neighborhood algorithms for budgeted and partial connected dominating set problems"
    - "deterministic parameterized connected vertex cover"
    - "submodular cost allocation problem and applications"

Connected through hyperedge HE-6 (induced by source paper "on the distributed computation of fractional connected dominating set packings"):
  with members:
    - "distributed connectivity decomposition"

Connected through hyperedge HE-7 (induced by source paper "broadcast throughput in radio networks routing vs network coding"):
  with members:
    - "randomized broadcast in radio networks with collision detection"
    - "a bound on the throughput of radio networks"
    - "bounds on contention management in radio networks"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-8 (induced by source paper "distributed connectivity decomposition"):
  with members:
    - "distributed approximation algorithms for weighted shortest paths"
    - "distributed minimum cut approximation"
    - "connected domatic packings in node capacitated graphs"
    - "lower bounds on information dissemination in dynamic networks"
    - "distributed verification and hardness of distributed approximation"
    - "efficient distributed random walks with applications"
    - "minimum cuts in near linear time"

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.DM(Discrete Mathematics)