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: "anti jamming communications using spectrum waterfall a deep reinforcement learning approach"
  Abstract: This letter investigates the problem of anti-jamming communications in dynamic and unknown environment through on-line learning. Different from existing studies which need to know (estimate) the jamming patterns and parameters, we use the spectrum waterfall, i.e., the raw spectrum environment, directly. Firstly, to cope with the challenge of infinite state of raw spectrum information, a deep anti-jamming Q-network is constructed. Then, a deep anti-jamming reinforcement learning algorithm is proposed to obtain the optimal anti-jamming strategies. Finally, simulation results validate the the proposed approach. The proposed approach is relying only on the local observed information and does not need to estimate the jamming patterns and parameters, which implies that it can be widely used various anti-jamming scenarios.

Connected through hyperedge HE-1 (induced by source paper "evolving neural networks through a reverse encoding tree"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "generalized binary search for split neighborly problems"
    - "vine an open source interactive data visualization tool for neuroevolution"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "complex networks from simple rewrite systems"

Connected through hyperedge HE-2 (induced by source paper "jam me if you can defeating jammer with deep dueling neural network architecture and ambient backscattering augmented communications"):
  with members:
    - "ambient backscatter communications a contemporary survey"
    - "cooperative access schemes for efficient swipt transmissions in cognitive radio networks"
    - "learning both weights and connections for efficient neural networks"
    - "dueling network architectures for deep reinforcement learning"
    - "a learning theoretic approach to energy harvesting communication system optimization"
    - "in band full duplex wireless challenges and opportunities"
    - "on the capacity of rate adaptive packetized wireless communication links under jamming"

Connected through hyperedge HE-3 (induced by source paper "physical adversarial attacks against end to end autoencoder communication systems"):
  with members:
    - "adversarial attacks on deep learning based radio signal classification"
    - "an introduction to deep learning for the physical layer"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "adversarial examples in the physical world"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"):
  with members:
    - "cooperative ambient backscatter communications for green internet of things"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "spectrum access in cognitive radio using a two stage reinforcement learning approach"
    - "spectrum sharing for internet of things a survey"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-5 (induced by source paper "a game theoretic learning framework for multi agent intelligent wireless networks"):
  with members:
    - "a collaborative multi agent reinforcement learning anti jamming algorithm in wireless networks"
    - "a machine learning framework for resource allocation assisted by cloud computing"

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.IT(Information Theory)