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: "deep value of information estimators for collaborative human machine information gathering"
  Abstract: Effective human-machine collaboration can significantly improve many learning and planning strategies for information gathering via fusion of 'hard' and 'soft' data originating from machine and human sensors, respectively. However, gathering the most informative data from human sensors without task overloading remains a critical technical challenge. In this context, Value of Information (VOI) is a crucial decision-theoretic metric for scheduling interaction with human sensors. We present a new Deep Learning based VOI estimation framework that can be used to schedule collaborative human-machine sensing with computationally efficient online inference and minimal policy hand-tuning. Supervised learning is used to train deep convolutional neural networks (CNNs) to extract hierarchical features from 'images' of belief spaces obtained via data fusion. These features can be associated with soft data query choices to reliably compute VOI for human interaction. The CNN framework is described in detail, and a performance comparison to a feature-based POMDP scheduling policy is provided. The practical feasibility of our method is also demonstrated on a mobile robotic search problem with language-based semantic human sensor inputs.

Connected through hyperedge HE-1 (induced by source paper "optimal continuous state pomdp planning with semantic observations a variational approach"):
  with members:
    - "closed loop bayesian semantic data fusion for collaborative human autonomy target search"
    - "near optimal belief space planning via t lqg"
    - "bandit algorithms for tree search"
    - "approximate safety verification and control of partially observable stochastic hybrid systems"
    - "a scalable method for solving high dimensional continuous pomdps using local approximation"
    - "perseus randomized point based value iteration for pomdps"

Connected through hyperedge HE-2 (induced by source paper "learning localized geometric features using 3d cnn an application to manufacturability analysis of drilled holes"):
  with members:
    - "recursive training of 2d 3d convolutional networks for neuronal boundary detection"
    - "predicting alzheimer s disease a neuroimaging study with 3d convolutional neural networks"
    - "occlusion edge detection in rgb d frames using deep convolutional networks"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep action sequence learning for causal shape transformation"):
  with members:
    - "spatial transformer networks"
    - "llnet a deep autoencoder approach to natural low light image enhancement"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "indoor semantic segmentation using depth information"

Connected through hyperedge HE-4 (induced by source paper "llnet a deep autoencoder approach to natural low light image enhancement"):
  with members:
    - "learning to deblur"
    - "indoor semantic segmentation using depth information"
    - "theano new features and speed improvements"

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.HC(Human-Computer Interaction)