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: "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
  Abstract: With the rapid development of fashion market, the customers' demands of customers for fashion recommendation are rising. In this paper, we aim to investigate a practical problem of fashion recommendation by answering the question “which item should we select to match with the given fashion items and form a compatible outfit”. The key to this problem is to estimate the outfit compatibility. Previous works which focus on the compatibility of two items or represent an outfit as a sequence fail to make full use of the complex relations among items in an outfit. To remedy this, we propose to represent an outfit as a graph. In particular, we construct a Fashion Graph, where each node represents a category and each edge represents interaction between two categories. Accordingly, each outfit can be represented as a subgraph by putting items into their corresponding category nodes. To infer the outfit compatibility from such a graph, we propose Node-wise Graph Neural Networks (NGNN) which can better model node interactions and learn better node representations. In NGNN, the node interaction on each edge is different, which is determined by parameters correlated to the two connected nodes. An attention mechanism is utilized to calculate the outfit compatibility score with learned node representations. NGNN can not only be used to model outfit compatibility from visual or textual modality but also from multiple modalities. We conduct experiments on two tasks: (1) Fill-in-the-blank: suggesting an item that matches with existing components of outfit; (2) Compatibility prediction: predicting the compatibility scores of given outfits. Experimental results demonstrate the great superiority of our proposed method over others.

Connected through hyperedge HE-1 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "toward explainable fashion recommendation"
    - "explainable fashion recommendation a semantic attribute region guided approach"
    - "context aware visual compatibility prediction"
    - "regularizing black box models for improved interpretability"
    - "outfit compatibility prediction and diagnosis with multi layered comparison network"
    - "learning type aware embeddings for fashion compatibility"
    - "methods for interpreting and understanding deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "fi gnn modeling feature interactions via graph neural networks for ctr prediction"):
  with members:
    - "session based recommendation with graph neural network"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "semi supervised compatibility learning across categories for clothing matching"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "product based neural networks for user response prediction over multi field categorical data"
    - "graph to sequence learning using gated graph neural networks"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"

Connected through hyperedge HE-3 (induced by source paper "deep set to set matching and learning"):
  with members:
    - "group reidentification with multigrained matching and integration"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "learning type aware embeddings for fashion compatibility"
    - "deep sets"
    - "recommending outfits from personal closet"
    - "fashionnet personalized outfit recommendation with deep neural network"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-4 (induced by source paper "hierarchical graph convolutional networks for semi supervised node classification"):
  with members:
    - "session based recommendation with graph neural network"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "hierarchical graph representation learning with differentiable pooling"
    - "mile a multi level framework for scalable graph embedding"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "large scale learnable graph convolutional networks"
    - "harp hierarchical representation learning for networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

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.IR(Information Retrieval)