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: "multi broadcasting under the sinr model"
  Abstract: We study the multi-broadcast problem in multi-hop wireless networks under the SINR model deployed in the 2D Euclidean plane. In multi-broadcast, there are $k$ initial rumours, potentially belonging to different nodes, that must be forwarded to all $n$ nodes of the network. Furthermore, in each round a node can only transmit a small message that could contain at most one initial rumor and $O(\log n)$ control bits. In order to be successfully delivered to a node, transmissions must satisfy the (Signal-to-Inference-and-Noise-Ratio) SINR condition and have sufficiently strong signal at the receiver. We present deterministic algorithms for multi-broadcast for different settings that reflect the different types of knowledge about the topology of the network available to the nodes: (i) the whole network topology (ii) their own coordinates and coordinates of their neighbors (iii) only their own coordinates, and (iv) only their own ids and the ids of their neighbors. For the former two settings, we present solutions that are scalable with respect to the diameter of the network and the polylogarithm of the network size, i.e., $\log^c n$ for some constant $c> 0$, while the solutions for the latter two have round complexity that is superlinear in the number of nodes. The last result is of special significance, as it is the first result for the SINR model that does not require nodes to know their coordinates in the plane (a very specialized type of knowledge), but intricately exploits the understanding that nodes are implanted in the 2D Euclidean plane.

Connected through hyperedge HE-1 (induced by source paper "deterministic protocols in the sinr model without knowledge of coordinates"):
  with members:
    - "achieving dilution without knowledge of coordinates in the sinr model"
    - "deterministic backbone creation in an sinr network without knowledge of location"
    - "distributed communication in bare bones wireless networks"
    - "almost optimal cover free families"
    - "beyond geometry towards fully realistic wireless models"
    - "on the impact of geometry on ad hoc communication in wireless networks"
    - "distributed deterministic broadcasting in uniform power ad hoc wireless networks"
    - "towards tight bounds for local broadcasting"

Connected through hyperedge HE-2 (induced by source paper "deterministic backbone creation in an sinr network without knowledge of location"):
  with members:
    - "achieving dilution without knowledge of coordinates in the sinr model"
    - "deterministic protocols in the sinr model without knowledge of coordinates"
    - "distributed bare bones communication in wireless networks"
    - "distributed communication in bare bones wireless networks"
    - "almost optimal cover free families"
    - "distributed deterministic broadcasting in wireless networks under the sinr model"

Connected through hyperedge HE-3 (induced by source paper "achieving dilution without knowledge of coordinates in the sinr model"):
  with members:
    - "deterministic protocols in the sinr model without knowledge of coordinates"
    - "deterministic backbone creation in an sinr network without knowledge of location"
    - "almost optimal cover free families"
    - "distributed deterministic broadcasting in wireless networks under the sinr model"

Connected through hyperedge HE-4 (induced by source paper "distributed bare bones communication in wireless networks"):
  with members:
    - "beyond geometry towards fully realistic wireless models"
    - "on the impact of geometry on ad hoc communication in wireless networks"
    - "distributed deterministic broadcasting in uniform power ad hoc wireless networks"

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.DS(Data Structures and Algorithms)