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: "heteromed heterogeneous information network for medical diagnosis"
  Abstract: With the recent availability of Electronic Health Records (EHR) and great opportunities they offer for advancing medical informatics, there has been growing interest in mining EHR for improving quality of care. Disease diagnosis due to its sensitive nature, huge costs of error, and complexity has become an increasingly important focus of research in past years. Existing studies model EHR by capturing co-occurrence of clinical events to learn their latent embeddings. However, relations among clinical events carry various semantics and contribute differently to disease diagnosis which gives precedence to a more advanced modeling of heterogeneous data types and relations in EHR data than existing solutions. To address these issues, we represent how high-dimensional EHR data and its rich relationships can be suitably translated into HeteroMed, a heterogeneous information network for robust medical diagnosis. Our modeling approach allows for straightforward handling of missing values and heterogeneity of data. HeteroMed exploits metapaths to capture higher level and semantically important relations contributing to disease diagnosis. Furthermore, it employs a joint embedding framework to tailor clinical event representations to the disease diagnosis goal. To the best of our knowledge, this is the first study to use Heterogeneous Information Network for modeling clinical data and disease diagnosis. Experimental results of our study show superior performance of HeteroMed compared to prior methods in prediction of exact diagnosis codes and general disease cohorts. Moreover, HeteroMed outperforms baseline models in capturing similarities of clinical events which are examined qualitatively through case studies.

Connected through hyperedge HE-1 (induced by source paper "hierarchical target attentive diagnosis prediction in heterogeneous information networks"):
  with members:
    - "hahe hierarchical attentive heterogeneous information network embedding"
    - "attention is all you need"
    - "benchmark of deep learning models on large healthcare mimic datasets"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-2 (induced by source paper "heterogeneous network approach to predict individuals mental health"):
  with members:
    - "the power of dynamic social networks to predict individuals mental health"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "social network structure is predictive of health and wellness"
    - "can we assess mental health through social media and smart devices addressing bias in methodology and evaluation"
    - "heterogeneous information network embedding for recommendation"
    - "instagram photos reveal predictive markers of depression"

Connected through hyperedge HE-3 (induced by source paper "higitclass keyword driven hierarchical classification of github repositories"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "categorizing the content of github readme files"
    - "universal language model fine tuning for text classification"
    - "weakly supervised hierarchical text classification"
    - "weakly supervised neural text classification"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-4 (induced by source paper "learning with heterogeneous side information fusion for recommender systems"):
  with members:
    - "taking human out of learning applications a survey on automated machine learning"
    - "collaborative filtering with social local models"
    - "heterogeneous information network embedding for recommendation"
    - "wide deep learning for recommender systems"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "fast low rank matrix learning with nonconvex regularization"
    - "a survey of heterogeneous information network analysis"
    - "deepwalk online learning of social representations"

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.AI(Artificial Intelligence)