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: "deep learning for joint source channel coding of text"
  Abstract: We consider the problem of joint source and channel coding of structured data such as natural language over a noisy channel. The typical approach to this problem in both theory and practice involves performing source coding to first compress the text and then channel coding to add robustness for the transmission across the channel. This approach is optimal in terms of minimizing end-to-end distortion with arbitrarily large block lengths of both the source and channel codes when transmission is over discrete memoryless channels. However, the optimality of this approach is no longer ensured for documents of finite length and limitations on the length of the encoding. We will show in this scenario that we can achieve lower word error rates by developing a deep learning based encoder and decoder. While the approach of separate source and channel coding would minimize bit error rates, our approach preserves semantic information of sentences by first embedding sentences in a semantic space where sentences closer in meaning are located closer together, and then performing joint source and channel coding on these embeddings.

Connected through hyperedge HE-1 (induced by source paper "an adaptive deep learning algorithm based autoencoder for interference channels"):
  with members:
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "one bit ofdm receivers via deep learning"
    - "deep reinforcement learning autoencoder with noisy feedback"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "training of deep neural networks based on distance measures using rmsprop"
    - "deep learning based mimo communications"

Connected through hyperedge HE-2 (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"
    - "approximating the void learning stochastic channel models from observation with variational generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "deep learning based polar code design"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-4 (induced by source paper "machine learning for geometrically consistent angular spread function estimation in massive mimo"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "deep learning methods for improved decoding of linear codes"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "on deep learning based channel decoding"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"

Connected through hyperedge HE-5 (induced by source paper "uplink downlink channel covariance transformations and precoding design for fdd massive mimo"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "5g new radio unveiling the essentials of the next generation wireless access technology"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"

Connected through hyperedge HE-6 (induced by source paper "deep learning based constellation optimization for physical network coding in two way relay networks"):
  with members:
    - "end to end deep learning of optical fiber communications"
    - "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"
    - "physical layer network coding tutorial survey and beyond"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "physical network coding in two way wireless relay channels"

Connected through hyperedge HE-7 (induced by source paper "successive refinement of images with deep joint source channel coding"):
  with members:
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "an introduction to deep learning for the physical layer"
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "variable rate image compression with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "user preference aware lossless data compression at the edge"):
  with members:
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "online edge caching and wireless delivery in fog aided networks with dynamic content popularity"
    - "edge caching wireless networks performance analysis and optimization"
    - "context aware small cell networks how social metrics improve wireless resource allocation"

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)