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: "kernelized hashcode representations for relation extraction"
  Abstract: Kernel methods have produced state-of-the-art results for a number of NLP tasks such as relation extraction, but suffer from poor scalability due to the high cost of computing kernel similarities between natural language structures. A recently proposed technique, kernelized locality-sensitive hashing (KLSH), can significantly reduce the computational cost, but is only applicable to classifiers operating on kNN graphs. Here we propose to use random subspaces of KLSH codes for efficiently constructing an explicit representation of NLP structures suitable for general classification methods. Further, we propose an approach for optimizing the KLSH model for classification problems by maximizing an approximation of mutual information between the KLSH codes (feature vectors) and the class labels. We evaluate the proposed approach on biomedical relation extraction datasets, and observe significant and robust improvements in accuracy w.r.t. state-of-the-art classifiers, along with drastic (orders-of-magnitude) speedup compared to conventional kernel methods.

Connected through hyperedge HE-1 (induced by source paper "nearly unsupervised hashcode representations for relation extraction"):
  with members:
    - "exploring semi supervised variational autoencoders for biomedical relation extraction"
    - "stochastic learning of nonstationary kernels for natural language modeling"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "extracting biomolecular interactions using semantic parsing of biomedical text"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-2 (induced by source paper "modeling psychotherapy dialogues with kernelized hashcode representations a nonparametric information theoretic approach"):
  with members:
    - "learning matching models with weak supervision for response selection in retrieval based chatbots"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "a hierarchical latent structure for variational conversation modeling"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "towards an automatic turing test learning to evaluate dialogue responses"
    - "adversarial learning for neural dialogue generation"

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)