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: "multi labeled relation extraction with attentive capsule network"
  Abstract: To disclose overlapped multiple relations from a sentence still keeps challenging. Most current works in terms of neural models inconveniently assuming that each sentence is explicitly mapped to a relation label, cannot handle multiple relations properly as the overlapped features of the relations are either ignored or very difficult to identify. To tackle with the new issue, we propose a novel approach for multi-labeled relation extraction with capsule network which acts considerably better than current convolutional or recurrent net in identifying the highly overlapped relations within an individual sentence. To better cluster the features and precisely extract the relations, we further devise attention-based routing algorithm and sliding-margin loss function, and embed them into our capsule network. The experimental results show that the proposed approach can indeed extract the highly overlapped features and achieve significant performance improvement for relation extraction comparing to the state-of-the-art works.

Connected through hyperedge HE-1 (induced by source paper "discovering protagonist of sentiment with aspect reconstructed capsule network"):
  with members:
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "transformation networks for target oriented sentiment classification"
    - "aspect level sentiment classification with attention over attention neural networks"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "dynamic routing between capsules"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-2 (induced by source paper "learning to infer entities properties and their relations from clinical conversations"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "extracting symptoms and their status from clinical conversations"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task learning for sequence tagging an empirical study"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "multi perspective inferrer reasoning sentences relationship from holistic perspective"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "dynamic past and future for neural machine translation"
    - "hypothesis only baselines in natural language inference"
    - "information aggregation via dynamic routing for sequence encoding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "stochastic answer networks for natural language inference"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"

Connected through hyperedge HE-4 (induced by source paper "improving distant supervised relation extraction by dynamic neural network"):
  with members:
    - "global textual relation embedding for relational understanding"
    - "fine tuning pre trained transformer language models to distantly supervised relation extraction"
    - "long tail relation extraction via knowledge graph embeddings and graph convolution networks"
    - "cross relation cross bag attention for distantly supervised relation extraction"
    - "distant supervision for relation extraction with linear attenuation simulation and non iid relevance embedding"
    - "combining distant and direct supervision for neural relation extraction"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "reinforcement learning for relation classification from noisy data"

Connected through hyperedge HE-5 (induced by source paper "capturing argument interaction in semantic role labeling with capsule networks"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "dependency or span end to end uniform semantic role labeling"
    - "polyglot semantic role labeling"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a full end to end semantic role labeler syntax agnostic over syntax aware"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "deep contextualized word representations"
    - "information aggregation via dynamic routing for sequence encoding"

Connected through hyperedge HE-6 (induced by source paper "hunt protagonist of sentiment sentiment analysis via capsule network with sentiment aspect reconstruction"):
  with members:
    - "a capsule network for recommendation and explaining what you like and dislike"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "dynamic routing between capsules"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)