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: "ndt neual decision tree towards fully functioned neural graph"
  Abstract: Though traditional algorithms could be embedded into neural architectures with the proposed principle of \cite{xiao2017hungarian}, the variables that only occur in the condition of branch could not be updated as a special case. To tackle this issue, we multiply the conditioned branches with Dirac symbol (i.e. $\mathbf{1}_{x>0}$), then approximate Dirac symbol with the continuous functions (e.g. $1 - e^{-\alpha|x|}$). In this way, the gradients of condition-specific variables could be worked out in the back-propagation process, approximately, making a fully functioned neural graph. Within our novel principle, we propose the neural decision tree \textbf{(NDT)}, which takes simplified neural networks as decision function in each branch and employs complex neural networks to generate the output in each leaf. Extensive experiments verify our theoretical analysis and demonstrate the effectiveness of our model.

Connected through hyperedge HE-1 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "fine grained classification of cervical cells using morphological and appearance based convolutional neural networks"
    - "maximum entropy fine grained classification"
    - "adaptive neural trees"

Connected through hyperedge HE-2 (induced by source paper "adaptive neural trees"):
  with members:
    - "multi layered gradient boosting decision trees"
    - "continuously constructive deep neural networks"
    - "large scale evolution of image classifiers"
    - "lifelong learning with dynamically expandable networks"
    - "efficient architecture search by network transformation"
    - "energy efficient amortized inference with cascaded deep classifiers"
    - "efficient multitask feature and relationship learning"
    - "branchynet fast inference via early exiting from deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "convexification of neural graph"):
  with members:
    - "hungarian layer logics empowered neural architecture"
    - "bilateral multi perspective matching for natural language sentences"
    - "semi supervised clustering for short text via deep representation learning"
    - "identity mappings in deep residual networks"
    - "lstm based deep learning models for non factoid answer selection"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "neural regression trees"):
  with members:
    - "adaptive neural trees"
    - "deep regression forests for age estimation"
    - "rule based machine learning methods for functional prediction"

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.NE(Neural and Evolutionary Computing)