human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "jamming resistant learning in wireless networks"):
  Abstract: We consider capacity maximization in wireless networks under adversarial interference conditions. There are n links, each consisting of a sender and a receiver, which repeatedly try to perform a successful transmission. In each time step, the success of attempted transmissions depends on interference conditions, which are captured by an interference model (e.g. the SINR model). Additionally, an adversarial jammer can render a (1-delta)-fraction of time steps unsuccessful. For this scenario, we analyze a framework for distributed learning algorithms to maximize the number of successful transmissions. Our main result is an algorithm based on no-regret learning converging to an O(1/delta)-approximation. It provides even a constant-factor approximation when the jammer exactly blocks a (1-delta)-fraction of time steps. In addition, we consider a stochastic jammer, for which we obtain a constant-factor approximation after a polynomial number of time steps. We also consider more general settings, in which links arrive and depart dynamically, and where each sender tries to reach multiple receivers. Our algorithms perform favorably in simulations.

Includes members:
  Member "competitive mac under adversarial sinr":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deterministic digital clustering of wireless ad hoc networks")
  Member "dynamic packet scheduling in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  Member "algorithms for wireless capacity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "wireless capacity and admission control in cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective wireless scheduling via hypergraph sketches")
  Member "wireless connectivity and capacity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "a jamming resistant mac protocol for multi hop wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepwifi cognitive wifi with deep learning")
  Member "on a game theoretic approach to capacity maximization in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reception capacity definitions game theory and hardness")
  Member "approximation algorithms for secondary spectrum auctions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fpt algorithms for diverse collections of hitting sets")

Question: Please tell me which class the source paper 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)