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: "learning to detect"
  Abstract: In this paper, we consider multiple-input-multiple-output detection using deep neural networks. We introduce two different deep architectures: a standard fully connected multi-layer network, and a detection network (DetNet), which is specifically designed for the task. The structure of DetNet is obtained by unfolding the iterations of a projected gradient descent algorithm into a network. We compare the accuracy and runtime complexity of the proposed approaches and achieve state-of-the-art performance while maintaining low computational requirements. Furthermore, we manage to train a single network to detect over an entire distribution of channels. Finally, we consider detection with soft outputs and show that the networks can easily be modified to produce soft decisions.

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming"):
  with members:
    - "learning optimal resource allocations in wireless systems"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "cnn based signal detection for banded linear systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep learning for radio resource allocation in multi cell networks"
    - "deep learning in physical layer communications"

Connected through hyperedge HE-2 (induced by source paper "direction concentration learning enhancing congruency in machine learning"):
  with members:
    - "a dilated inception network for visual saliency prediction"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "natural gradient deep q learning"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "dual glance model for deciphering social relationships"
    - "gradient episodic memory for continual learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration"):
  with members:
    - "a deep learning framework for optimization of miso downlink beamforming"
    - "artificial intelligence enabled cellular networks a critical path to beyond 5g and 6g"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "learning to optimize training deep neural networks for interference management"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "model driven deep learning for physical layer communications"

Connected through hyperedge HE-4 (induced by source paper "constructing deep neural networks with a priori knowledge of wireless tasks"):
  with members:
    - "optimizing wireless systems using unsupervised and reinforced unsupervised deep learning"
    - "deep learning for ul dl channel calibration in generic massive mimo systems"
    - "learning to optimize with unsupervised learning training deep neural networks for urllc"
    - "structure of deep neural networks with a priori information in wireless tasks"
    - "an lstm network for highway trajectory prediction"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "model driven deep learning for physical layer communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-5 (induced by source paper "learning aided deep path prediction for sphere decoding in large mimo systems"):
  with members:
    - "a model driven deep learning network for mimo detection"
    - "no downlink pilots are needed in tdd massive mimo"

Connected through hyperedge HE-6 (induced by source paper "deep learning based wireless resource allocation with application to vehicular networks"):
  with members:
    - "learning to branch accelerating resource allocation in wireless networks"
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "learning optimal resource allocations in wireless systems"
    - "deep learning based end to end wireless communication systems with conditional gan as unknown channel"
    - "distributed power control for large energy harvesting networks a multi agent deep reinforcement learning approach"
    - "power allocation in multi user cellular networks deep reinforcement learning approaches"
    - "toward intelligent vehicular networks a machine learning framework"
    - "machine learning in the air"

Connected through hyperedge HE-7 (induced by source paper "deep learning for csi feedback based on superimposed coding"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "artificial intelligence aided receiver for a cp free ofdm system design simulation and experimental test"
    - "enabling fdd massive mimo through deep learning based channel prediction"
    - "deep learning in physical layer communications"
    - "mimo channel information feedback using deep recurrent network"
    - "multi task learning as multi objective optimization"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "deep learning based csi feedback approach for time varying massive mimo channels"

Connected through hyperedge HE-8 (induced by source paper "a regression approach to certain information transmission problems"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "one bit ofdm receivers via deep learning"
    - "deepcode feedback codes via deep learning"
    - "learning the mmse channel estimator"
    - "a model driven deep learning network for mimo detection"
    - "a two step learning and interpolation method for location based channel database"
    - "mixed adc massive mimo"

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)