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: "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.

Connected through hyperedge HE-1 (induced by source paper "effective wireless scheduling via hypergraph sketches"):
  with members:
    - "wireless link capacity under shadowing and fading"
    - "the price of local power control in wireless scheduling"
    - "how well can graphs represent wireless interference"
    - "beyond geometry towards fully realistic wireless models"
    - "online independent set beyond the worst case secretaries prophets and periods"
    - "dynamic packet scheduling in wireless networks"
    - "on the complexity of the minimum latency scheduling problem on the euclidean plane"
    - "algorithms for wireless capacity"

Connected through hyperedge HE-2 (induced by source paper "two dimensional anti jamming mobile communication based on reinforcement learning"):
  with members:
    - "deep reinforcement learning with macro actions"

Connected through hyperedge HE-3 (induced by source paper "smoothness for simultaneous composition of mechanisms with admission"):
  with members:
    - "learning and efficiency in games with dynamic population"
    - "no regret learning in bayesian games"
    - "learning in auctions regret is hard envy is easy"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "tight bounds for the price of anarchy of simultaneous first price auctions"
    - "bounding the inefficiency of outcomes in generalized second price auctions"
    - "composable and efficient mechanisms"
    - "simultaneous auctions are almost efficient"

Connected through hyperedge HE-4 (induced by source paper "an online approach to dynamic channel access and transmission scheduling"):
  with members:
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "sufficient conditions on the optimality of myopic sensing in opportunistic channel access a unifying framework"
    - "to stay or to switch multiuser dynamic channel access"
    - "performance and convergence of multi user online learning"
    - "on a game theoretic approach to capacity maximization in wireless networks"

Connected through hyperedge HE-5 (induced by source paper "beyond geometry towards fully realistic wireless models"):
  with members:
    - "wireless scheduling algorithms in complex environments"
    - "distributed deterministic broadcasting in uniform power ad hoc wireless networks"
    - "online independent set beyond the worst case secretaries prophets and periods"
    - "approximation algorithms for wireless link scheduling with flexible data rates"
    - "distributed connectivity of wireless networks"
    - "mlls minimum length link scheduling under physical interference model"
    - "the power of non uniform wireless power"
    - "a fully distributed algorithm for throughput performance in wireless networks"

Connected through hyperedge HE-6 (induced by source paper "wireless scheduling algorithms in complex environments"):
  with members:
    - "beyond geometry towards fully realistic wireless models"
    - "online independent set beyond the worst case secretaries prophets and periods"
    - "nearly optimal bounds for distributed wireless scheduling in the sinr model"
    - "wireless capacity and admission control in cognitive radio"
    - "wireless capacity with oblivious power in general metrics"
    - "a constant factor approximation for wireless capacity maximization with power control in the sinr model"
    - "on a game theoretic approach to capacity maximization in 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)