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: "exploration on generating traditional chinese medicine prescription from symptoms with an end to end method"
  Abstract: Traditional Chinese Medicine (TCM) is an influential form of medical treatment in China and surrounding areas. In this paper, we propose a TCM prescription generation task that aims to automatically generate a herbal medicine prescription based on textual symptom descriptions. Sequence-to-sequence (seq2seq) model has been successful in dealing with conditional sequence generation tasks like dialogue generation. We explore a potential end-to-end solution to the TCM prescription generation task using seq2seq models. However, experiments show that directly applying seq2seq model leads to unfruitful results due to the severe repetition problem. To solve the problem, we propose a novel architecture for the decoder with masking and coverage mechanism. The experimental results demonstrate that the proposed method is effective in diversifying the outputs, which significantly improves the F1 score by nearly 10 points (8.34 on test set 1 and 10.23 on test set 2).

Connected through hyperedge HE-1 (induced by source paper "syndrome aware herb recommendation with multi graph convolution network"):
  with members:
    - "beyond clicks modeling multi relational item graph for session based target behavior prediction"
    - "session based recommendation with graph neural network"
    - "kgat knowledge graph attention network for recommendation"
    - "graph neural networks for social recommendation"
    - "neural graph collaborative filtering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "socialgcn an efficient graph convolutional network based model for social recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"

Connected through hyperedge HE-2 (induced by source paper "many could be better than all a novel instance oriented algorithm for multi modal multi label problem"):
  with members:
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "review driven multi label music style classification by exploiting style correlations"):
  with members:
    - "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"
    - "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
    - "multi label music genre classification from audio text and images using deep features"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "primal meaning recommendation via on line encyclopedia"):
  with members:
    - "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "skip thought vectors"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "primal meaning recommendation for chinese words and phrases via descriptions in on line encyclopedia"):
  with members:
    - "embedding words and senses together via joint knowledge enhanced training"
    - "skip thought vectors"
    - "sequence to sequence learning with neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "automatic construction of chinese herbal prescription from tongue image via cnns and auxiliary latent therapy topics"):
  with members:
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "a survey on deep learning in medical image analysis"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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.CL(Computation and Language)