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: "algorithms for dynamic spectrum access with learning for cognitive radio"
  Abstract: We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperatively tries to exploit vacancies in primary (licensed) channels whose occupancies follow a Markovian evolution. We first consider the scenario where the cognitive users have perfect knowledge of the distribution of the signals they receive from the primary users. For this problem, we obtain a greedy channel selection and access policy that maximizes the instantaneous reward, while satisfying a constraint on the probability of interfering with licensed transmissions. We also derive an analytical universal upper bound on the performance of the optimal policy. Through simulation, we show that our scheme achieves good performance relative to the upper bound and improved performance relative to an existing scheme. #R##N#We then consider the more practical scenario where the exact distribution of the signal from the primary is unknown. We assume a parametric model for the distribution and develop an algorithm that can learn the true distribution, still guaranteeing the constraint on the interference probability. We show that this algorithm outperforms the naive design that assumes a worst case value for the parameter. We also provide a proof for the convergence of the learning algorithm.

Connected through hyperedge HE-1 (induced by source paper "spectrum access in cognitive radio using a two stage reinforcement learning approach"):
  with members:
    - "a brief introduction to machine learning for engineers"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "multi agent q learning of channel selection in multi user cognitive radio systems a two by two case"

Connected through hyperedge HE-2 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "boolean compressed sensing and noisy group testing"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"

Connected through hyperedge HE-3 (induced by source paper "active classification for pomdps a kalman like state estimator"):
  with members:
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "near optimal adaptive compressed sensing"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "noisy bayesian active learning"
    - "sensor scheduling for energy efficient target tracking in sensor networks"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-4 (induced by source paper "the cognitive compressive sensing problem"):
  with members:
    - "compressive link acquisition in multiuser communications"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "sub nyquist sampling of short pulses"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"

Connected through hyperedge HE-5 (induced by source paper "nonlinear pomdps for active state tracking with sensing costs"):
  with members:
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "active classification for pomdps a kalman like state estimator"
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sensor scheduling for energy efficient target tracking in sensor networks"

Connected through hyperedge HE-6 (induced by source paper "controlled sensing a myopic fisher information sensor selection algorithm"):
  with members:
    - "active sequential hypothesis testing"
    - "controlled sensing for multihypothesis testing"
    - "sensor scheduling for energy efficient target tracking in sensor networks"

Connected through hyperedge HE-7 (induced by source paper "on design of opportunistic spectrum access in the presence of reactive primary users"):
  with members:
    - "cognitive interference management in retransmission based wireless networks"
    - "on active learning and supervised transmission of spectrum sharing based cognitive radios by exploiting hidden primary radio feedback"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "joint design and separation principle for opportunistic spectrum access in the presence of sensing errors"

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.NI(Networking and Internet Architecture)