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: "an attention based bi gru capsnet model for hypernymy detection between compound entities"
  Abstract: Named entities are usually composable and extensible. Typical examples are names of symptoms and diseases in medical areas. To distinguish these entities from general entities, we name them \textit{compound entities}. In this paper, we present an attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities. Our model consists of several important components. To avoid the out-of-vocabulary problem, English words or Chinese characters in compound entities are fed into the bidirectional gated recurrent units. An attention mechanism is designed to focus on the differences between the two compound entities. Since there are some different cases in hypernymy relationship between compound entities, capsule network is finally employed to decide whether the hypernymy relationship exists or not. Experimental results demonstrate

Connected through hyperedge HE-1 (induced by source paper "question answering based clinical text structuring using pre trained language model"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "neural semi markov conditional random fields for robust character based part of speech tagging"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "fast and accurate recognition of chinese clinical named entities with residual dilated convolutions"

Connected through hyperedge HE-2 (induced by source paper "capsule networks need an improved routing algorithm"):
  with members:
    - "capsnet comparative performance evaluation for image classification"
    - "videocapsulenet a simplified network for action detection"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "fast capsnet for lung cancer screening"
    - "group equivariant capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"

Connected through hyperedge HE-3 (induced by source paper "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"):
  with members:
    - "object localization and motion transfer learning with capsules"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "improved explainability of capsule networks relevance path by agreement"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "sparse unsupervised capsules generalize better"
    - "dynamic routing between capsules"
    - "dropmax adaptive variational softmax"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)