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: "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"
  Abstract: Channel modeling is a critical topic when considering designing, learning, or evaluating the performance of any communications system. Prior work in manual modulation system design or learned modulation system design has largely focused on simplified analytic channel models such as additive white Gaussian noise (AWGN) or Rayleigh fading channels, in more recent work we consider the usage of generative adversarial networks (GANs) to jointly approximate of a wireless channel response and design an efficient encoding and decoding of information to robustly survive it. In this paper, we focus more specifically on characterizing how well a GAN can capture the stochastic nature of a typical wireless channel response, and the topic of effectively designing the network and loss function to accurately capture its stochastic behavior in a probabilistic sense. We illustrate the problems with certain approaches and share results capturing the performance of such as system over a range channel distributions.

Connected through hyperedge HE-1 (induced by source paper "deep learning based end to end wireless communication systems with conditional gan as unknown channel"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning in physical layer communications"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "end to end learning of communications systems without a channel model"
    - "a model driven deep learning network for mimo detection"
    - "deep learning for joint source channel coding of text"

Connected through hyperedge HE-2 (induced by source paper "a very brief introduction to machine learning with applications to communication systems"):
  with members:
    - "proactive received power prediction using machine learning and depth images for mmwave networks"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "machine learning at the edge a data driven architecture with applications to 5g cellular networks"
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning in physical layer communications"
    - "deep learning methods for improved decoding of linear codes"

Connected through hyperedge HE-3 (induced by source paper "model free training of end to end communication systems"):
  with members:
    - "one bit ofdm receivers via deep learning"
    - "achievable information rates for nonlinear fiber communication via end to end autoencoder learning"
    - "deep joint source channel coding for wireless image transmission"
    - "neural joint source channel coding"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "a learning approach to wireless information and power transfer signal and system design"

Connected through hyperedge HE-4 (induced by source paper "channel agnostic end to end learning based communication systems with conditional gan"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "end to end learning of communications systems without a channel model"
    - "a model driven deep learning network for mimo detection"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning autoencoder with noisy feedback"):
  with members:
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "adam a method for stochastic optimization"

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.LG(Machine Learning)